Template for Electronic Submission to ACS Journals



Assessment of Hydration Thermodynamics at Protein Interfaces with Grid Cell TheoryGeorgios Gerogiokasa, Michelle W. Y. Southeyb, Michael P. Mazanetzb, Alexander Heifetzb, Michael Bodkinb, Richard J. Lawb, Richard H. Henchmanc, and J. Michela*aEaStCHEM School of Chemistry, Joseph Black Building, The King's Buildings, Edinburgh, EH9 3JJ, UK. E-mail: mail@bEvotec (UK) Limited, 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4SA.cManchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom and School of Chemistry, The University of Manchester, Oxford Road, Manchester M13 9PL, United KingdomAbstract: Molecular dynamics simulations have been analyzed with the Grid Cell Theory (GCT) method to spatially resolve the binding enthalpies and entropies of water molecules at the interface of 17 structurally diverse proteins. Correlations between computed energetics and structural descriptors have been sought to facilitate the development of simple models of protein hydration. Little correlation was found between GCT computed binding enthalpies and continuum electrostatics calculations. A simple count of contacts with functional groups in charged amino-acids correlates well with enhanced water stabilization, but the stability of water near hydrophobic and polar residues depends markedly on its coordination environment. The positions of X-ray resolved water molecules correlate with computed high density hydration sites, but many unresolved waters are significantly stabilized at the protein surfaces. A defining characteristic of ligand-binding pockets compared to non-binding pockets was a greater solvent-accessible volume, but average water thermodynamic properties were not distinctive from other interfacial regions. Interfacial water molecules are frequently stabilized by enthalpy and destabilized entropy with respect to bulk, but counter-examples occasionally occur. Overall detailed inspection of the local coordinating environment appears necessary to gauge thermodynamic stability of water in protein structures. Introduction Water plays a crucial role in the structure and dynamics of proteins. Water has been implicated as a mediator of interactions between different protein surfaces, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"20eu2g555r","properties":{"formattedCitation":"{\\rtf \\super 1\\nosupersub{}}","plainCitation":"1"},"citationItems":[{"id":206,"uris":[""],"uri":[""],"itemData":{"id":206,"type":"article-journal","title":"Charge, hydrophobicity, and confined water: putting past simulations into a simple theoretical frameworkThis paper is one of a selection of papers published in this special issue entitled “Canadian Society of Biochemistry, Molecular & Cellular Biology 52nd Annual Meeting — Protein Folding: Principles and Diseases” and has undergone the Journal's usual peer review process.","container-title":"Biochemistry and Cell Biology","page":"359-369","volume":"88","issue":"2","source":"NRC Research Press","abstract":"Water permeates all life, and mediates forces that are essential to the process of macromolecular self-assembly. Predicting these forces in a given biological context is challenging, since water organizes itself differently next to charged and hydrophobic surfaces, both of which are typically at play on the nanoscale in vivo. In this work, we present a simple statistical mechanical model for the forces water mediates between different confining surfaces, and demonstrate that the model qualitatively unifies a wide range of phenomena known in the simulation literature, including several cases of protein folding under confinement., L’eau baigne toute vie et sert d’intermédiaire aux forces essentielles au processus d’autoassemblage macromoléculaire. C’est un véritable défi que de prédire ces forces dans un contexte biologique donné car l’eau s’organise différemment à proximité de surfaces chargées et hydrophobes, les deux étant typiquement sollicitées à l’échelle nanométrique in vivo. Dans ce travail, nous présentons un modèle statistique mécanique simple des forces réglées par l’intermédiaire de l’eau entre différentes surfaces de confinement, et nous démontrons que le modèle unifie quantitativement un vaste spectre de phénomènes connus dans la littérature sur la simulation, y compris plusieurs cas de repliement des protéines en condition de confinement.","DOI":"10.1139/O09-187","ISSN":"0829-8211","shortTitle":"Charge, hydrophobicity, and confined water","journalAbbreviation":"Biochem. Cell Biol.","author":[{"family":"England","given":"Jeremy L."},{"family":"Pande","given":"Vijay S."}],"issued":{"date-parts":[["2010",3,23]]}}}],"schema":""} 1 and is a major driver for protein folding through the burial of hydrophobic side chains of amino acids. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"alsgr82m1","properties":{"formattedCitation":"{\\rtf \\super 2\\nosupersub{}}","plainCitation":"2"},"citationItems":[{"id":209,"uris":[""],"uri":[""],"itemData":{"id":209,"type":"article-journal","title":"Protein folding and association: Insights from the interfacial and thermodynamic properties of hydrocarbons","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"281-296","volume":"11","issue":"4","source":"Wiley Online Library","abstract":"We demonstrate in this work that the surface tension, water-organic solvent, transfer-free energies and the thermodynamics of melting of linear alkanes provide fundamental insights into the nonpolar driving forces for protein folding and protein binding reactions. We first develop a model for the curvature dependence of the hydrophobic effect and find that the macroscopic concept of interfacial free energy is applicable at the molecular level. Application of a well-known relationship involving surface tension and adhesion energies reveals that dispersion forces play little or no net role in hydrophobic interactions; rather, the standard model of disruption of water structure (entropically driven at 25°C) is correct. The hydrophobic interaction is found, in agreement with the classical picture, to provide a major driving force for protein folding. Analysis of the melting behavior of hydrocarbons reveals that close packing of the protein interior makes only a small free energy contribution to folding because the enthalpic gain resulting from increased dispersion interactions (relative to the liquid) is countered by the freezing of side chain motion. The identical effect should occur in association reactions, which may provide an enormous simplification in the evaluation of binding energies. Protein binding reactions, even between nearly planar or concave/convex interfaces, are found to have effective hydrophobicities considerably smaller than the prediction based on macroscopic surface tension. This is due to the formation of a concave collar region that usually accompanies complex formation. This effect may preclude the formation of complexes between convex surfaces.","DOI":"10.1002/prot.340110407","ISSN":"1097-0134","shortTitle":"Protein folding and association","journalAbbreviation":"Proteins","language":"en","author":[{"family":"Nicholls","given":"Anthony"},{"family":"Sharp","given":"Kim A."},{"family":"Honig","given":"Barry"}],"issued":{"date-parts":[["1991",12,1]]}}}],"schema":""} 2 Understanding water-protein interactions relates to protein function and is important for enzyme catalysis, as well as DNA-water interactions ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2lprs57poq","properties":{"formattedCitation":"{\\rtf \\super 3\\nosupersub{}}","plainCitation":"3"},"citationItems":[{"id":219,"uris":[""],"uri":[""],"itemData":{"id":219,"type":"article-journal","title":"DNA–Water Interactions Distinguish Messenger RNA Genes from Transfer RNA Genes","container-title":"Journal of the American Chemical Society","page":"8814-8816","volume":"134","issue":"21","source":"ACS Publications","abstract":"Physicochemical properties of DNA sequences as a guide to developing insights into genome organization has received little attention. Here, we utilize the energetics of DNA to further advance the knowledge on its language at a molecular level. Specifically, we ask the question whether physicochemical properties of different functional units on genomes differ. We extract intramolecular and solvation energies of different DNA base pair steps from a comprehensive set of molecular dynamics simulations. We then investigate the solvation behavior of DNA sequences coding for mRNAs and tRNAs. Distinguishing mRNA genes from tRNA genes is a tricky problem in genome annotation without assumptions on length of DNA and secondary structure of the product of transcription. We find that solvation energetics of DNA behaves as an extremely efficient property in discriminating 2?063 537 genes coding for mRNAs from 56?251 genes coding for tRNAs in all (～1500) completely sequenced prokaryotic genomes.","DOI":"10.1021/ja3020956","ISSN":"0002-7863","journalAbbreviation":"J. Am. Chem. Soc.","author":[{"family":"Khandelwal","given":"Garima"},{"family":"Jayaram","given":"B."}],"issued":{"date-parts":[["2012",5,30]]}}}],"schema":""} 3 and molecular recognition of various events including protein-DNA, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1dhmo5p71a","properties":{"formattedCitation":"{\\rtf \\super 4,5\\nosupersub{}}","plainCitation":"4,5"},"citationItems":[{"id":215,"uris":[""],"uri":[""],"itemData":{"id":215,"type":"article-journal","title":"Energetics of the protein-DNA-water interaction","container-title":"BMC Structural Biology","page":"4","volume":"7","source":"BioMed Central","abstract":"To understand the energetics of the interaction between protein and DNA we analyzed 39 crystallographically characterized complexes with the HINT (Hydropathic INTeractions) computational model. HINT is an empirical free energy force field based on solvent partitioning of small molecules between water and 1-octanol. Our previous studies on protein-ligand complexes demonstrated that free energy predictions were significantly improved by taking into account the energetic contribution of water molecules that form at least one hydrogen bond with each interacting species.","DOI":"10.1186/1472-6807-7-4","ISSN":"1472-6807","journalAbbreviation":"BMC Structural Biology","author":[{"family":"Spyrakis","given":"Francesca"},{"family":"Cozzini","given":"Pietro"},{"family":"Bertoli","given":"Chiara"},{"family":"Marabotti","given":"Anna"},{"family":"Kellogg","given":"Glen E."},{"family":"Mozzarelli","given":"Andrea"}],"issued":{"date-parts":[["2007"]]}}},{"id":212,"uris":[""],"uri":[""],"itemData":{"id":212,"type":"article-journal","title":"Do water molecules mediate protein-DNA recognition?1","container-title":"Journal of Molecular Biology","page":"619-632","volume":"314","issue":"3","source":"ScienceDirect","abstract":"A comprehensive analysis of interfacial water molecules in the structures of 109 unique protein-DNA complexes is presented together with a new view on their role in protein-DNA recognition. Location of interfacial water molecules as reported in the crystal structures and as emerging from a series of molecular dynamics studies on protein-DNA complexes with explicit solvent and counterions, was analyzed based on their acceptor, donor hydrogen bond relationships with the atoms and residues of the macromolecules, electrostatic field calculations and packing density considerations. Water molecules for the purpose of this study have been categorized into four classes: viz. (I) those that contact both the protein and the DNA simultaneously and thus mediate recognition directly; (II) those that contact either the protein or the DNA exclusively via hydrogen bonds solvating each solute separately; (III) those that contact the hydrophobic groups in either the protein or the DNA; and, lastly (IV) those that contact another water molecule. Of the 17,963 crystallographic water molecules under examination, about 6 % belong to class I and 76 % belong to class II. About three-fourths of class I and class II water molecules are exclusively associated with hydrogen bond acceptor atoms of both protein and DNA. Noting that DNA is polyanionic, it is significant that a majority of the crystallographically observed water molecules as well as those from molecular dynamics simulations should be involved in facilitating binding by screening unfavorable electrostatics. Less than 2 % of the reported water molecules occur between hydrogen bond donor atoms of protein and acceptor atoms of DNA. These represent cases where protein atoms cannot reach out to DNA to make favorable hydrogen bond interactions due to packing/structural restrictions and interfacial water molecules provide an extension to side-chains to accomplish hydrogen bonding.","DOI":"10.1006/jmbi.2001.5154","ISSN":"0022-2836","shortTitle":"Do water molecules mediate protein-DNA recognition?","journalAbbreviation":"Journal of Molecular Biology","author":[{"family":"Reddy","given":"Ch. Koti"},{"family":"Das","given":"Achintya"},{"family":"Jayaram","given":"B."}],"issued":{"date-parts":[["2001",11,30]]}}}],"schema":""} 4,5 protein-protein ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2pvlegqg41","properties":{"formattedCitation":"{\\rtf \\super 6\\nosupersub{}}","plainCitation":"6"},"citationItems":[{"id":225,"uris":[""],"uri":[""],"itemData":{"id":225,"type":"article-journal","title":"The atomic structure of protein-protein recognition sites1","container-title":"Journal of Molecular Biology","page":"2177-2198","volume":"285","issue":"5","source":"ScienceDirect","abstract":"The non-covalent assembly of proteins that fold separately is central to many biological processes, and differs from the permanent macromolecular assembly of protein subunits in oligomeric proteins. We performed an analysis of the atomic structure of the recognition sites seen in 75 protein-protein complexes of known three-dimensional structure: 24 protease-inhibitor, 19 antibody-antigen and 32 other complexes, including nine enzyme-inhibitor and 11 that are involved in signal transduction.\n\nThe size of the recognition site is related to the conformational changes that occur upon association. Of the 75 complexes, 52 have “standard-size” interfaces in which the total area buried by the components in the recognition site is 1600 (±400) ?2. In these complexes, association involves only small changes of conformation. Twenty complexes have “large” interfaces burying 2000 to 4660 ?2, and large conformational changes are seen to occur in those cases where we can compare the structure of complexed and free components. The average interface has approximately the same non-polar character as the protein surface as a whole, and carries somewhat fewer charged groups. However, some interfaces are significantly more polar and others more non-polar than the average.\n\nOf the atoms that lose accessibility upon association, half make contacts across the interface and one-third become fully inaccessible to the solvent. In the latter case, the Voronoi volume was calculated and compared with that of atoms buried inside proteins. The ratio of the two volumes was 1.01 (±0.03) in all but 11 complexes, which shows that atoms buried at protein-protein interfaces are close-packed like the protein interior. This conclusion could be extended to the majority of interface atoms by including solvent positions determined in high-resolution X-ray structures in the calculation of Voronoi volumes. Thus, water molecules contribute to the close-packing of atoms that insure complementarity between the two protein surfaces, as well as providing polar interactions between the two proteins.","DOI":"10.1006/jmbi.1998.2439","ISSN":"0022-2836","journalAbbreviation":"Journal of Molecular Biology","author":[{"family":"Conte","given":"Loredana Lo"},{"family":"Chothia","given":"Cyrus"},{"family":"Janin","given":"Jo?l"}],"issued":{"date-parts":[["1999",2,5]]}}}],"schema":""} 6 and protein-ligand interactions. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"92aergn9c","properties":{"formattedCitation":"{\\rtf \\super 7\\nosupersub{}}","plainCitation":"7"},"citationItems":[{"id":228,"uris":[""],"uri":[""],"itemData":{"id":228,"type":"article-journal","title":"Analysis of Ligand-Bound Water Molecules in High-Resolution Crystal Structures of Protein?Ligand Complexes","container-title":"Journal of Chemical Information and Modeling","page":"668-675","volume":"47","issue":"2","source":"ACS Publications","abstract":"We have performed a comprehensive analysis of water molecules at the protein?ligand interfaces observed in 392 high-resolution crystal structures. There are a total of 1829 ligand-bound water molecules in these 392 complexes; 18% are surface water molecules, and 72% are interfacial water molecules. The number of ligand-bound water molecules in each complex structure ranges from 0 to 21 and has an average of 4.6. Of these interfacial water molecules, 76% are considered to be bridging water molecules, characterized by having polar interactions with both ligand and protein atoms. Among a number of factors that may influence the number of ligand-bound water molecules, the polar van der Waals (vdw) surface area of ligands has the highest Pearson linear correlation coefficient of 0.63. Our regression analysis predicted that one more ligand-bound water molecule is expected for every additional 24 ?2 in the polar vdw surface area of the ligand. In contrast to the observation that the resolution is the primary factor influencing the number of water molecules in crystallographic models of proteins, we found that there is only a weak relationship between the number of ligand-bound water molecules and the resolution of the crystal structures. An analysis of the isotropic B factors of buried ligand-bound water molecules suggested that, when water molecules have fewer than two polar interactions with the protein?ligand complex, they are more mobile than protein atoms in the crystal structures; when they have more than three polar interactions, they are significantly less mobile than protein atoms.","DOI":"10.1021/ci6003527","ISSN":"1549-9596","journalAbbreviation":"J. Chem. Inf. Model.","author":[{"family":"Lu","given":"Yipin"},{"family":"Wang","given":"Renxiao"},{"family":"Yang","given":"Chao-Yie"},{"family":"Wang","given":"Shaomeng"}],"issued":{"date-parts":[["2007",3,1]]}}}],"schema":""} 7 Greater understanding of the role played by water in molecular recognition opens up new avenues for the creation of novel therapeutics.A key question relates to the thermodynamics properties of water at the interface of biomolecules, sometimes also called biological water. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"19lht2fqu9","properties":{"formattedCitation":"{\\rtf \\super 8\\nosupersub{}}","plainCitation":"8"},"citationItems":[{"id":231,"uris":[""],"uri":[""],"itemData":{"id":231,"type":"article-journal","title":"Biological Water or Rather Water in Biology?","container-title":"The Journal of Physical Chemistry Letters","page":"2449-2451","volume":"6","issue":"13","source":"ACS Publications","DOI":"10.1021/acs.jpclett.5b01143","ISSN":"1948-7185","journalAbbreviation":"J. Phys. Chem. Lett.","author":[{"family":"Jungwirth","given":"Pavel"}],"issued":{"date-parts":[["2015",7,2]]}}}],"schema":""} 8 Biological water is often defined as a hydration layer around proteins. This hydration layer is distinct from bulk water both thermodynamically and dynamically, as shown from terahertz spectroscopy data, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1e5bt94k3s","properties":{"formattedCitation":"{\\rtf \\super 9\\nosupersub{}}","plainCitation":"9"},"citationItems":[{"id":234,"uris":[""],"uri":[""],"itemData":{"id":234,"type":"article-journal","title":"Collective THz dynamics in living Escherichia coli cells","container-title":"Chemical Physics","collection-title":"Neutron Scattering Highlights on Water and Biological Systems","page":"84-88","volume":"424","source":"ScienceDirect","abstract":"We have employed neutron Brillouin spectroscopy to study coherent collective density fluctuations in the biological macromolecular components of living Escherichia coli cells. To highlight the contribution of the macromolecular material alone, a suitably prepared mixture of light and heavy water was exploited to cancel the scattering length of intracellular water. The present results indicate that the cellular biomaterial sustains THz coherent density fluctuations, characterised by a propagating mode travelling at about 3600 m/s and by a localised mode at energies between 4 and 7 meV. A comparison with both hydration water and simpler biomolecules, such as proteins or DNA, brings further support to the idea that the dynamical coupling between biomolecular structures and biological water provides the delicate dynamical adaptation needed to achieve a full biological functionality. Finally, the behaviour of the damping factors of the observed collective modes strengthens the dynamical similarity of biological systems with glass-forming materials.","DOI":"10.1016/j.chemphys.2013.06.020","ISSN":"0301-0104","journalAbbreviation":"Chemical Physics","author":[{"family":"Sebastiani","given":"F."},{"family":"Orecchini","given":"A."},{"family":"Paciaroni","given":"A."},{"family":"Jasnin","given":"M."},{"family":"Zaccai","given":"G."},{"family":"Moulin","given":"M."},{"family":"Haertlein","given":"M."},{"family":"De Francesco","given":"A."},{"family":"Petrillo","given":"C."},{"family":"Sacchetti","given":"F."}],"issued":{"date-parts":[["2013",10,16]]}}}],"schema":""} 9 and molecular dynamics. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1ci0ij25po","properties":{"formattedCitation":"{\\rtf \\super 10\\nosupersub{}}","plainCitation":"10"},"citationItems":[{"id":237,"uris":[""],"uri":[""],"itemData":{"id":237,"type":"article-journal","title":"Water Dynamics in Protein Hydration Shells: The Molecular Origins of the Dynamical Perturbation","container-title":"The Journal of Physical Chemistry. B","page":"7715-7729","volume":"118","issue":"28","source":"PubMed Central","abstract":", Protein hydration shell dynamics\nplay an important role in biochemical\nprocesses including protein folding, enzyme function, and molecular\nrecognition. We present here a comparison of the reorientation dynamics\nof individual water molecules within the hydration shell of a series\nof globular proteins: acetylcholinesterase, subtilisin Carlsberg,\nlysozyme, and ubiquitin. Molecular dynamics simulations and analytical\nmodels are used to access site-resolved information on hydration shell\ndynamics and to elucidate the molecular origins of the dynamical perturbation\nof hydration shell water relative to bulk water. We show that all\nfour proteins have very similar hydration shell dynamics, despite\ntheir wide range of sizes and functions, and differing secondary structures.\nWe demonstrate that this arises from the similar local surface topology\nand surface chemical composition of the four proteins, and that such\nlocal factors alone are sufficient to rationalize the hydration shell\ndynamics. We propose that these conclusions can be generalized to\na wide range of globular proteins. We also show that protein conformational\nfluctuations induce a dynamical heterogeneity within the hydration\nlayer. We finally address the effect of confinement on hydration shell\ndynamics via a site-resolved analysis and connect our results to experiments\nvia the calculation of two-dimensional infrared spectra.","DOI":"10.1021/jp409805p","ISSN":"1520-6106","note":"PMID: 24479585\nPMCID: PMC4103960","shortTitle":"Water Dynamics in Protein Hydration Shells","journalAbbreviation":"J Phys Chem B","author":[{"family":"Fogarty","given":"Aoife\nC."},{"family":"Laage","given":"Damien"}],"issued":{"date-parts":[["2014",7,17]]},"PMID":"24479585","PMCID":"PMC4103960"}}],"schema":""} 10 It is important to understand the extent of this hydration layer and whether the majority of cellular water does differ greatly from bulk. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1plekdbgqh","properties":{"formattedCitation":"{\\rtf \\super 10\\nosupersub{}}","plainCitation":"10"},"citationItems":[{"id":237,"uris":[""],"uri":[""],"itemData":{"id":237,"type":"article-journal","title":"Water Dynamics in Protein Hydration Shells: The Molecular Origins of the Dynamical Perturbation","container-title":"The Journal of Physical Chemistry. B","page":"7715-7729","volume":"118","issue":"28","source":"PubMed Central","abstract":", Protein hydration shell dynamics\nplay an important role in biochemical\nprocesses including protein folding, enzyme function, and molecular\nrecognition. We present here a comparison of the reorientation dynamics\nof individual water molecules within the hydration shell of a series\nof globular proteins: acetylcholinesterase, subtilisin Carlsberg,\nlysozyme, and ubiquitin. Molecular dynamics simulations and analytical\nmodels are used to access site-resolved information on hydration shell\ndynamics and to elucidate the molecular origins of the dynamical perturbation\nof hydration shell water relative to bulk water. We show that all\nfour proteins have very similar hydration shell dynamics, despite\ntheir wide range of sizes and functions, and differing secondary structures.\nWe demonstrate that this arises from the similar local surface topology\nand surface chemical composition of the four proteins, and that such\nlocal factors alone are sufficient to rationalize the hydration shell\ndynamics. We propose that these conclusions can be generalized to\na wide range of globular proteins. We also show that protein conformational\nfluctuations induce a dynamical heterogeneity within the hydration\nlayer. We finally address the effect of confinement on hydration shell\ndynamics via a site-resolved analysis and connect our results to experiments\nvia the calculation of two-dimensional infrared spectra.","DOI":"10.1021/jp409805p","ISSN":"1520-6106","note":"PMID: 24479585\nPMCID: PMC4103960","shortTitle":"Water Dynamics in Protein Hydration Shells","journalAbbreviation":"J Phys Chem B","author":[{"family":"Fogarty","given":"Aoife\nC."},{"family":"Laage","given":"Damien"}],"issued":{"date-parts":[["2014",7,17]]},"PMID":"24479585","PMCID":"PMC4103960"}}],"schema":""} 10 Most experimental and molecular dynamics studies suggest only the first two solvation shells significantly differ from bulk water when the oxygen density of water molecules is considered, but orientational correlations may be longer-ranged. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2d4tpc9tkm","properties":{"formattedCitation":"{\\rtf \\super 11,12\\nosupersub{}}","plainCitation":"11,12"},"citationItems":[{"id":243,"uris":[""],"uri":[""],"itemData":{"id":243,"type":"article-journal","title":"Hydration shells of proteins probed by depolarized light scattering and dielectric spectroscopy: orientational structure is significant, positional structure is not","container-title":"The Journal of Chemical Physics","page":"22D501","volume":"141","issue":"22","source":"PubMed","abstract":"Water interfacing hydrated proteins carry properties distinct from those of the bulk and is often described as a separate entity, a \"biological water.\" We address here the question of which dynamical and structural properties of hydration water deserve this distinction. The study focuses on different aspects of the density and orientational fluctuations of hydration water and the ability to separate them experimentally by combining depolarized light scattering with dielectric spectroscopy. We show that the dynamics of the density fluctuations of the hydration shells reflect the coupled dynamics of the solute and solvent and do not require a special distinction as \"biological water.\" The orientations of shell water molecules carry dramatically different physics and do require a separation into a sub-ensemble. Depending on the property considered, the perturbation of water's orientational structure induced by the protein propagates 3-5 hydration shells into the bulk at normal temperature.","DOI":"10.1063/1.4895544","ISSN":"1089-7690","note":"PMID: 25494772","shortTitle":"Hydration shells of proteins probed by depolarized light scattering and dielectric spectroscopy","journalAbbreviation":"J Chem Phys","language":"eng","author":[{"family":"Martin","given":"Daniel R."},{"family":"Matyushov","given":"Dmitry V."}],"issued":{"date-parts":[["2014",12,14]]},"PMID":"25494772"}},{"id":240,"uris":[""],"uri":[""],"itemData":{"id":240,"type":"article-journal","title":"New Insights into the Role of Water in Biological Function: Studying Solvated Biomolecules Using Terahertz Absorption Spectroscopy in Conjunction with Molecular Dynamics Simulations","container-title":"Journal of the American Chemical Society","page":"12800-12807","volume":"136","issue":"37","source":"ACS Publications","abstract":"In life science, water is the ubiquitous solvent, sometimes even called the “matrix of life”. There is increasing experimental and theoretical evidence that solvation water is not a passive spectator in biomolecular processes. New experimental techniques can quantify how water interacts with biomolecules and, in doing so, differs from “bulk” water. Terahertz (THz) absorption spectroscopy has turned out to be a powerful tool to study (bio)molecular hydration. The main concepts that have been developed in the recent years to describe the underlying solute-induced sub-picosecond dynamics of the hydration shell are discussed herein. Moreover, we highlight recent findings that show the significance of hydrogen bond dynamics for the function of antifreeze proteins and for molecular recognition. In all of these examples, a gradient of water motion toward functional sites of proteins is observed, the so-called “hydration funnel”. By means of molecular dynamics simulations, we provide new evidence for a specific water–protein coupling as the cause of the observed dynamical heterogeneity. The efficiency of the coupling at THz frequencies is explained in terms of a two-tier (short- and long-range) solute–solvent interaction.","DOI":"10.1021/ja504441h","ISSN":"0002-7863","shortTitle":"New Insights into the Role of Water in Biological Function","journalAbbreviation":"J. Am. Chem. Soc.","author":[{"family":"Conti Nibali","given":"Valeria"},{"family":"Havenith","given":"Martina"}],"issued":{"date-parts":[["2014",9,17]]}}}],"schema":""} 11,12 For water in the first hydration layer of biomolecules there is clear coupling between dynamics and thermodynamics. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1lj1q8gt8c","properties":{"formattedCitation":"{\\rtf \\super 13\\nosupersub{}}","plainCitation":"13"},"citationItems":[{"id":248,"uris":[""],"uri":[""],"itemData":{"id":248,"type":"article-journal","title":"Cell water dynamics on multiple time scales","container-title":"Proceedings of the National Academy of Sciences","page":"6266-6271","volume":"105","issue":"17","source":"","abstract":"Water–biomolecule interactions have been extensively studied in dilute solutions, crystals, and rehydrated powders, but none of these model systems may capture the behavior of water in the highly organized intracellular milieu. Because of the experimental difficulty of selectively probing the structure and dynamics of water in intact cells, radically different views about the properties of cell water have proliferated. To resolve this long-standing controversy, we have measured the 2H spin relaxation rate in living bacteria cultured in D2O. The relaxation data, acquired in a wide magnetic field range (0.2 mT–12 T) and analyzed in a model-independent way, reveal water dynamics on a wide range of time scales. Contradicting the view that a substantial fraction of cell water is strongly perturbed, we find that ≈85% of cell water in Escherichia coli and in the extreme halophile Haloarcula marismortui has bulk-like dynamics. The remaining ≈15% of cell water interacts directly with biomolecular surfaces and is motionally retarded by a factor 15 ± 3 on average, corresponding to a rotational correlation time of 27 ps. This dynamic perturbation is three times larger than for small monomeric proteins in solution, a difference we attribute to secluded surface hydration sites in supramolecular assemblies. The relaxation data also show that a small fraction (≈0.1%) of cell water exchanges from buried hydration sites on the microsecond time scale, consistent with the current understanding of protein hydration in solutions and crystals.","DOI":"10.1073/pnas.0709585105","ISSN":"0027-8424, 1091-6490","note":"PMID: 18436650","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Persson","given":"Erik"},{"family":"Halle","given":"Bertil"}],"issued":{"date-parts":[["2008",4,29]]},"PMID":"18436650"}}],"schema":""} 13 The present study is primarily concerned with the correlation of interfacial water thermodynamics with protein structural descriptors. The dataset includes approximately 85,000 hydration sites across the interface of 17 proteins. Many of these proteins are popular drug targets such as: HMG-COA reductase, PDE5, cyclooxygenase, caspase1, MDM2, kinases (CDK, cAbl), thrombin, HIV, neuraminidase, penicilin binding protein. This dataset overlaps with a dataset used by Beuming et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"203h9b94v2","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":196,"uris":[""],"uri":[""],"itemData":{"id":196,"type":"article-journal","title":"Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"871-883","volume":"80","issue":"3","source":"CrossRef","DOI":"10.1002/prot.23244","ISSN":"08873585","language":"en","author":[{"family":"Beuming","given":"Thijs"},{"family":"Che","given":"Ye"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Shanmugasundaram","given":"Veerabahu"},{"family":"Sherman","given":"Woody"}],"issued":{"date-parts":[["2012",3]]}}}],"schema":""} 14 to evaluate thermodynamics of hydration sites using the inhomogeneous fluid solvation theory (IFST) as implemented in the Watermap software. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"b4c1f1ebm","properties":{"formattedCitation":"{\\rtf \\super 15\\nosupersub{}}","plainCitation":"15"},"citationItems":[{"id":252,"uris":[""],"uri":[""],"itemData":{"id":252,"type":"article-journal","title":"Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding","container-title":"Proceedings of the National Academy of Sciences","page":"808-813","volume":"104","issue":"3","source":"","abstract":"The thermodynamic properties and phase behavior of water in confined regions can vary significantly from that observed in the bulk. This is particularly true for systems in which the confinement is on the molecular-length scale. In this study, we use molecular dynamics simulations and a powerful solvent analysis technique based on inhomogenous solvation theory to investigate the properties of water molecules that solvate the confined regions of protein active sites. Our simulations and analysis indicate that the solvation of protein active sites that are characterized by hydrophobic enclosure and correlated hydrogen bonds induce atypical entropic and enthalpic penalties of hydration. These penalties apparently stabilize the protein–ligand complex with respect to the independently solvated ligand and protein, which leads to enhanced binding affinities. Our analysis elucidates several challenging cases, including the super affinity of the streptavidin–biotin system.","DOI":"10.1073/pnas.0610202104","ISSN":"0027-8424, 1091-6490","note":"PMID: 17204562","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Young","given":"Tom"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Berne","given":"Bruce J."},{"family":"Friesner","given":"Richard A."}],"issued":{"date-parts":[["2007",1,16]]},"PMID":"17204562"}}],"schema":""} 15 A secondary objective of the present study was thus to compare IFST computed water thermodynamic properties to those produced by the Grid Cell Theory (GCT) methodology, as implemented in the software Nautilus. GCT is a newly developed method to investigate hydration thermodynamics from a single molecular dynamics (MD) or Monte Carlo (MC) trajectory. GCT is a spatial discretization of the cell theory method developed by Henchman. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"11q1gcg5s2","properties":{"formattedCitation":"{\\rtf \\super 16\\nosupersub{}}","plainCitation":"16"},"citationItems":[{"id":14,"uris":[""],"uri":[""],"itemData":{"id":14,"type":"article-journal","title":"Free energy of liquid water from a computer simulation via cell theory","container-title":"The Journal of Chemical Physics","page":"064504","volume":"126","issue":"6","source":"scitation.","abstract":"A method to calculate the free energy of water from computer simulation is presented. Based on cell theory, it approximates the potential energy surface sampled in the simulation by an anisotropic six-dimensional harmonic potential to model the three hindered translations and three hindered rotations of a single rigid water molecule. The potential is parametrized from the magnitude of the forces and torques measured in the simulation. The entropy of these six harmonic oscillators is calculated and summed with a conformational term to give the total entropy. Combining this with the simulation enthalpy yields the free energy. The six water models examined are TIP3P, SPC, TIP4P, SPC/E, TIP5P, and TIP4P-Ew. The results reproduce experiment well: free energies for all models are within 1.6 kJ mol ? 1 and entropies are within 3.6 J K ? 1 mol ? 1 . Approximately two-thirds of the entropy comes from translation, a third from rotation, and 5% from conformation. Vibrational frequencies match those in the experimental infrared spectrum and assist in their assignment. Intermolecular quantum effects are found to be small, with free energies for the classical oscillator lying 0.5 – 0.7 kJ mol ? 1 higher than in the quantum case. Molecular displacements and vibrational and zero point energies are also calculated. Altogether, these results validate the harmonic oscillator as a quantitative model for the liquid state.","DOI":"10.1063/1.2434964","ISSN":"0021-9606, 1089-7690","author":[{"family":"Henchman","given":"Richard H."}],"issued":{"date-parts":[["2007",2,14]]}}}],"schema":""} 16 GCT has recently been validated on small molecules, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"207einjh83","properties":{"formattedCitation":"{\\rtf \\super 17\\nosupersub{}}","plainCitation":"17"},"citationItems":[{"id":51,"uris":[""],"uri":[""],"itemData":{"id":51,"type":"article-journal","title":"Prediction of Small Molecule Hydration Thermodynamics with Grid Cell Theory","container-title":"Journal of Chemical Theory and Computation","page":"35-48","volume":"10","issue":"1","source":"ACS Publications","abstract":"An efficient methodology has been developed to quantify water energetics by analysis of explicit solvent molecular simulations of organic and biomolecular systems. The approach, grid cell theory (GCT), relies on a discretization of the cell theory methodology on a three-dimensional grid to spatially resolve the density, enthalpy, and entropy of water molecules in the vicinity of solute(s) of interest. Entropies of hydration are found to converge more efficiently than enthalpies of hydration. GCT predictions of free energies of hydration on a data set of small molecules are strongly correlated with thermodynamic integration predictions. Agreement with the experiment is comparable for both approaches. A key advantage of GCT is its ability to provide from a single simulation insightful graphical analyses of spatially resolved components of the enthalpies and entropies of hydration.","DOI":"10.1021/ct400783h","ISSN":"1549-9618","journalAbbreviation":"J. Chem. Theory Comput.","author":[{"family":"Gerogiokas","given":"Georgios"},{"family":"Calabro","given":"Gaetano"},{"family":"Henchman","given":"Richard H."},{"family":"Southey","given":"Michelle W. Y."},{"family":"Law","given":"Richard J."},{"family":"Michel","given":"Julien"}],"issued":{"date-parts":[["2014",1,14]]}}}],"schema":""} 17 model binding sites, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2dfk28nec6","properties":{"formattedCitation":"{\\rtf \\super 18\\nosupersub{}}","plainCitation":"18"},"citationItems":[{"id":128,"uris":[""],"uri":[""],"itemData":{"id":128,"type":"article-journal","title":"Evaluation of Host–Guest Binding Thermodynamics of Model Cavities with Grid Cell Theory","container-title":"Journal of Chemical Theory and Computation","page":"4055-4068","volume":"10","issue":"9","source":"ACS Publications","abstract":"A previously developed cell theory model of liquid water was used to evaluate the excess thermodynamic properties of confined clusters of water molecules. The results are in good agreement with reference thermodynamic integration calculations, suggesting that the model is adequate to probe the thermodynamic properties of water at interfaces or in cavities. Next, the grid cell theory (GCT) method was applied to elucidate the thermodynamic signature of nonpolar association for a range of idealized host?guest systems. Polarity and geometry of the host cavities were systematically varied, and enthalpic and entropic solvent components were spatially resolved for detailed graphical analyses. Perturbations in the thermodynamic properties of water molecules upon guest binding are restricted to the immediate vicinity of the guest in solvent-exposed cavities, whereas longer-ranged perturbations are observed in buried cavities. Depending on the polarity and geometry of the host, water displacement by a nonpolar guest makes a small or large enthalpic or entropic contribution to the free energy of binding. Thus, no assumptions about the thermodynamic signature of the hydrophobic effect can be made in general. Overall the results warrant further applications of GCT to more complex systems such as protein?ligand complexes.","DOI":"10.1021/ct500368p","ISSN":"1549-9618","journalAbbreviation":"J. Chem. Theory Comput.","author":[{"family":"Michel","given":"Julien"},{"family":"Henchman","given":"Richard H."},{"family":"Gerogiokas","given":"Georgios"},{"family":"Southey","given":"Michelle W. Y."},{"family":"Mazanetz","given":"Michael P."},{"family":"Law","given":"Richard J."}],"issued":{"date-parts":[["2014",9,9]]}}}],"schema":""} 18 as well as protein-ligand complexes. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2e8ougqve1","properties":{"formattedCitation":"{\\rtf \\super 19\\nosupersub{}}","plainCitation":"19"},"citationItems":[{"id":22,"uris":[""],"uri":[""],"itemData":{"id":22,"type":"article-journal","title":"Evaluation of water displacement energetics in protein binding sites with grid cell theory","container-title":"Physical Chemistry Chemical Physics","page":"8416-8426","volume":"17","issue":"13","source":"pubs.","abstract":"Excess free energies, enthalpies and entropies of water in protein binding sites were computed via classical simulations and Grid Cell Theory (GCT) analyses for three pairs of congeneric ligands in complex with the proteins scytalone dehydratase, p38α MAP kinase and EGFR kinase respectively. Comparative analysis is of interest since the binding modes for each ligand pair differ in the displacement of one binding site water molecule, but significant variations in relative binding affinities are observed. Protocols that vary in their use of restraints on protein and ligand atoms were compared to determine the influence of protein–ligand flexibility on computed water structure and energetics, and to assess protocols for routine analyses of protein–ligand complexes. The GCT-derived binding affinities correctly reproduce experimental trends, but the magnitude of the predicted changes in binding affinities is exaggerated with respect to results from a previous Monte Carlo Free Energy Perturbation study. Breakdown of the GCT water free energies into enthalpic and entropic components indicates that enthalpy changes dominate the observed variations in energetics. In EGFR kinase GCT analyses revealed that replacement of a pyrimidine by a cyanopyridine perturbs water energetics up three hydration shells away from the ligand.","DOI":"10.1039/C4CP05572A","ISSN":"1463-9084","journalAbbreviation":"Phys. Chem. Chem. Phys.","language":"en","author":[{"family":"Gerogiokas","given":"G."},{"family":"Southey","given":"M. W. Y."},{"family":"Mazanetz","given":"M. P."},{"family":"Hefeitz","given":"A."},{"family":"Bodkin","given":"M."},{"family":"Law","given":"R. J."},{"family":"Michel","given":"J."}],"issued":{"date-parts":[["2015",3,23]]}}}],"schema":""} 19 Other novel analyses that are reported here include correlation of Poisson-Boltzmann electrostatics with water binding thermodynamics, water thermodynamics in binding-sites, and correlations between water binding enthalpies and entropies. The results help build a comprehensive picture of the hydration thermodynamics at protein interfaces, and suggest how complexities may be subsumed into simpler structural descriptors. Theory and MethodsGrid cell theory Grid cell theory has been used to compute binding free energies as reported in previous work. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2la1suku08","properties":{"formattedCitation":"{\\rtf \\super 17\\uc0\\u8211{}19\\nosupersub{}}","plainCitation":"17–19"},"citationItems":[{"id":22,"uris":[""],"uri":[""],"itemData":{"id":22,"type":"article-journal","title":"Evaluation of water displacement energetics in protein binding sites with grid cell theory","container-title":"Physical Chemistry Chemical Physics","page":"8416-8426","volume":"17","issue":"13","source":"pubs.","abstract":"Excess free energies, enthalpies and entropies of water in protein binding sites were computed via classical simulations and Grid Cell Theory (GCT) analyses for three pairs of congeneric ligands in complex with the proteins scytalone dehydratase, p38α MAP kinase and EGFR kinase respectively. Comparative analysis is of interest since the binding modes for each ligand pair differ in the displacement of one binding site water molecule, but significant variations in relative binding affinities are observed. Protocols that vary in their use of restraints on protein and ligand atoms were compared to determine the influence of protein–ligand flexibility on computed water structure and energetics, and to assess protocols for routine analyses of protein–ligand complexes. The GCT-derived binding affinities correctly reproduce experimental trends, but the magnitude of the predicted changes in binding affinities is exaggerated with respect to results from a previous Monte Carlo Free Energy Perturbation study. Breakdown of the GCT water free energies into enthalpic and entropic components indicates that enthalpy changes dominate the observed variations in energetics. In EGFR kinase GCT analyses revealed that replacement of a pyrimidine by a cyanopyridine perturbs water energetics up three hydration shells away from the ligand.","DOI":"10.1039/C4CP05572A","ISSN":"1463-9084","journalAbbreviation":"Phys. Chem. Chem. Phys.","language":"en","author":[{"family":"Gerogiokas","given":"G."},{"family":"Southey","given":"M. W. Y."},{"family":"Mazanetz","given":"M. P."},{"family":"Hefeitz","given":"A."},{"family":"Bodkin","given":"M."},{"family":"Law","given":"R. J."},{"family":"Michel","given":"J."}],"issued":{"date-parts":[["2015",3,23]]}}},{"id":128,"uris":[""],"uri":[""],"itemData":{"id":128,"type":"article-journal","title":"Evaluation of Host–Guest Binding Thermodynamics of Model Cavities with Grid Cell Theory","container-title":"Journal of Chemical Theory and Computation","page":"4055-4068","volume":"10","issue":"9","source":"ACS Publications","abstract":"A previously developed cell theory model of liquid water was used to evaluate the excess thermodynamic properties of confined clusters of water molecules. The results are in good agreement with reference thermodynamic integration calculations, suggesting that the model is adequate to probe the thermodynamic properties of water at interfaces or in cavities. Next, the grid cell theory (GCT) method was applied to elucidate the thermodynamic signature of nonpolar association for a range of idealized host?guest systems. Polarity and geometry of the host cavities were systematically varied, and enthalpic and entropic solvent components were spatially resolved for detailed graphical analyses. Perturbations in the thermodynamic properties of water molecules upon guest binding are restricted to the immediate vicinity of the guest in solvent-exposed cavities, whereas longer-ranged perturbations are observed in buried cavities. Depending on the polarity and geometry of the host, water displacement by a nonpolar guest makes a small or large enthalpic or entropic contribution to the free energy of binding. Thus, no assumptions about the thermodynamic signature of the hydrophobic effect can be made in general. Overall the results warrant further applications of GCT to more complex systems such as protein?ligand complexes.","DOI":"10.1021/ct500368p","ISSN":"1549-9618","journalAbbreviation":"J. Chem. Theory Comput.","author":[{"family":"Michel","given":"Julien"},{"family":"Henchman","given":"Richard H."},{"family":"Gerogiokas","given":"Georgios"},{"family":"Southey","given":"Michelle W. Y."},{"family":"Mazanetz","given":"Michael P."},{"family":"Law","given":"Richard J."}],"issued":{"date-parts":[["2014",9,9]]}}},{"id":51,"uris":[""],"uri":[""],"itemData":{"id":51,"type":"article-journal","title":"Prediction of Small Molecule Hydration Thermodynamics with Grid Cell Theory","container-title":"Journal of Chemical Theory and Computation","page":"35-48","volume":"10","issue":"1","source":"ACS Publications","abstract":"An efficient methodology has been developed to quantify water energetics by analysis of explicit solvent molecular simulations of organic and biomolecular systems. The approach, grid cell theory (GCT), relies on a discretization of the cell theory methodology on a three-dimensional grid to spatially resolve the density, enthalpy, and entropy of water molecules in the vicinity of solute(s) of interest. Entropies of hydration are found to converge more efficiently than enthalpies of hydration. GCT predictions of free energies of hydration on a data set of small molecules are strongly correlated with thermodynamic integration predictions. Agreement with the experiment is comparable for both approaches. A key advantage of GCT is its ability to provide from a single simulation insightful graphical analyses of spatially resolved components of the enthalpies and entropies of hydration.","DOI":"10.1021/ct400783h","ISSN":"1549-9618","journalAbbreviation":"J. Chem. Theory Comput.","author":[{"family":"Gerogiokas","given":"Georgios"},{"family":"Calabro","given":"Gaetano"},{"family":"Henchman","given":"Richard H."},{"family":"Southey","given":"Michelle W. Y."},{"family":"Law","given":"Richard J."},{"family":"Michel","given":"Julien"}],"issued":{"date-parts":[["2014",1,14]]}}}],"schema":""} 17–19 In the approach outlined the density, enthalpy, entropy and free energy of water are evaluated for an arbitrary region of space s around a system of interest X. Binding free energy, enthalpy and entropy of water defined here refer to the process where water enters a particular hydration site of the protein(s) from bulk concentration. Here the computation of the binding free energy involves three steps.First, parameters of water molecules inside s are evaluated. For each frame f, cell parameters of the Nf water molecules i ∈ s are determined. These cell parameters are: the magnitude of the components of the intermolecular forces |Fij| and torques |τij| along the principal axes j (j = x,y,z) of the water molecule i, the orientational number Ωiori of the water molecule i, the protein-water interaction energy ?HiX, and the water-water interaction energy ?Hiw In line with preceding studies, the water-water interaction energy term is half the average interaction energy with other water molecules, minus half the average interaction energy in bulk water. These quantities are equated to enthalpies because contributions from pressure-volume terms were neglected. Detailed expressions for these quantities may be found elsewhere. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"278qm55gjp","properties":{"formattedCitation":"{\\rtf \\super 19\\nosupersub{}}","plainCitation":"19"},"citationItems":[{"id":22,"uris":[""],"uri":[""],"itemData":{"id":22,"type":"article-journal","title":"Evaluation of water displacement energetics in protein binding sites with grid cell theory","container-title":"Physical Chemistry Chemical Physics","page":"8416-8426","volume":"17","issue":"13","source":"pubs.","abstract":"Excess free energies, enthalpies and entropies of water in protein binding sites were computed via classical simulations and Grid Cell Theory (GCT) analyses for three pairs of congeneric ligands in complex with the proteins scytalone dehydratase, p38α MAP kinase and EGFR kinase respectively. Comparative analysis is of interest since the binding modes for each ligand pair differ in the displacement of one binding site water molecule, but significant variations in relative binding affinities are observed. Protocols that vary in their use of restraints on protein and ligand atoms were compared to determine the influence of protein–ligand flexibility on computed water structure and energetics, and to assess protocols for routine analyses of protein–ligand complexes. The GCT-derived binding affinities correctly reproduce experimental trends, but the magnitude of the predicted changes in binding affinities is exaggerated with respect to results from a previous Monte Carlo Free Energy Perturbation study. Breakdown of the GCT water free energies into enthalpic and entropic components indicates that enthalpy changes dominate the observed variations in energetics. In EGFR kinase GCT analyses revealed that replacement of a pyrimidine by a cyanopyridine perturbs water energetics up three hydration shells away from the ligand.","DOI":"10.1039/C4CP05572A","ISSN":"1463-9084","journalAbbreviation":"Phys. Chem. Chem. Phys.","language":"en","author":[{"family":"Gerogiokas","given":"G."},{"family":"Southey","given":"M. W. Y."},{"family":"Mazanetz","given":"M. P."},{"family":"Hefeitz","given":"A."},{"family":"Bodkin","given":"M."},{"family":"Law","given":"R. J."},{"family":"Michel","given":"J."}],"issued":{"date-parts":[["2015",3,23]]}}}],"schema":""} 19Second, parameters for volume elements within s are determined. To do so the region s is decomposed into Ns voxels of volume V(k). Properties of each k voxel are given by equation 1: Ak=f=1Mi=1NfAi Ikimax1,f=1Mi=1NfIki, (1)where typically Ai = Fij, τij, and Ωiori,?HiX, and, ?Hiw. Ik(i) is an indicator function which is equal to 1 if water molecule i is in voxel k, and 0 otherwise. Whether a water molecule i is within a voxel is determined by inspection of the Cartesian coordinates of the oxygen atom of the water molecule. Finally, M is the number of frames in the analyzed trajectory. The average number of water molecules within voxel k is given by equation 2: Nwk=1Mf=1Mi=1NfIk(i) (2)Third, binding thermodynamic properties of s are evaluated. Equations 3 and 4 give the solute and solvent components of the enthalpy of binding of region s: ΔHXs=k=1NsNwk?HX(k) (3) ΔHws=k=1NsNwk?Hw(k) (4) The enthalpy of binding in region s, is given by equation 5: ΔHw,Xs=ΔHXs+ΔHws (5)The average number of water molecules within s is computed with equation 6: Nws=k=1NsNwk (6)The average orientational numbers and forces/torques for region s, are given by equation 7:As=1Nwsk=1NsNwkAk, (7)where A = Fj, τj, and Ωori. Additionally, the minimum value for Ωori(s) is always 1 in this work. The calculation of the orientational number of each water molecule i in frame f is based on the generalized Pauling’s residual ice entropy model given by equation 8, where Na is the number of hydrogen bond acceptors within 3.4 ? around water i. This equation is used unless there is a solute polar or charged atom in the coordination shell of the water, in which case equation 9 is applied instead. Ωori=Na(Na-1)2Na-2Na2, (8) Ωori=Naeff(Naeff-1)2Nabulk-2Nabulk2-pHBX, (9)where Naeffis the effective coordination number, Nabulk is the coordination number of bulk water. The number of hydrogen bond acceptors Na is given by:Na=NX+Nws+Nwb (10)where Nx is the number of solute acceptor atoms within the cutoff, Nws is the number of first hydration shell water molecules within the cutoff, and Nwb the number of remaining water molecules. Next, the ratios of each type of acceptors that are hydrogen bonded to water i is given by equation 11. pHBX=NXHBNX ; pHBws=NwsHBNws ; pHBwb=NwbHBNwb (11)And the effective coordination number is then obtained from equation 12:Naeff=NXHB+NwsHB+NwbHBmax?(pHBX,pHBws,pHBwb) (12)With the orientations, forces, and torques equations 13-14 are used to give the entropic components: ΔSw,Xs,ori=NwskBlnΩori(s)Ωori(bulk) (13) ΔSw,Xs,vib=NwskBlnj=13Fj(bulk)Fj(s) (14) ΔSw,Xs,lib=NwskBlnj=13τj(bulk)τj(s) (15)where Ωori(bulk), Fj(bulk), τj(bulk) (j = x, y, z) are the cell parameters for the simulated water model in bulk conditions. Summation of the components in equation 16 allows the computation of the entropy of binding within region s: ΔSw,Xs= ΔSw,Xs,ori+ΔSw,Xs,vib+ ΔSw,Xs,lib (16)Finally, the addition of the enthalpic and entropic components gives the binding free energy of water within region s:ΔGw,Xs=ΔHw,Xs-TΔSw,Xs (17)Nautilus is a trajectory analysis software that implements equations 1-17. For some analyses, the enthalpy, entropy and free energy values of a region s were further normalized by number of waters present within region s, and this is denoted by the superscript symbol ‘w’.Nautilus, has several dependencies including the molecular simulation framework Sire, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2ivtu57o2m","properties":{"formattedCitation":"{\\rtf \\super 20\\nosupersub{}}","plainCitation":"20"},"citationItems":[{"id":153,"uris":[""],"uri":[""],"itemData":{"id":153,"type":"book","title":"Sire Molecular Simulation Framework, Revision 1786","URL":"","author":[{"family":"Woods","given":"Christopher"},{"family":"Michel","given":"Julien"}]}}],"schema":""} 20 and the MDtraj python package. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"guiu3tkm1","properties":{"formattedCitation":"{\\rtf \\super 21\\nosupersub{}}","plainCitation":"21"},"citationItems":[{"id":149,"uris":[""],"uri":[""],"itemData":{"id":149,"type":"article-journal","title":"MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories","container-title":"Biophysical Journal","page":"1528-1532","volume":"109","issue":"8","source":"ScienceDirect","abstract":"As molecular dynamics (MD) simulations continue to evolve into powerful computational tools for studying complex biomolecular systems, the necessity of flexible and easy-to-use software tools for the analysis of these simulations is growing. We have developed MDTraj, a modern, lightweight, and fast software package for analyzing MD simulations. MDTraj reads and writes trajectory data in a wide variety of commonly used formats. It provides a large number of trajectory analysis capabilities including minimal root-mean-square-deviation calculations, secondary structure assignment, and the extraction of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between MD data and the rapidly growing collection of industry-standard statistical analysis and visualization tools in Python. MDTraj is a powerful and user-friendly software package that simplifies the analysis of MD data and connects these datasets with the modern interactive data science software ecosystem in Python.","DOI":"10.1016/j.bpj.2015.08.015","ISSN":"0006-3495","shortTitle":"MDTraj","journalAbbreviation":"Biophysical Journal","author":[{"family":"McGibbon","given":"Robert?T."},{"family":"Beauchamp","given":"Kyle?A."},{"family":"Harrigan","given":"Matthew?P."},{"family":"Klein","given":"Christoph"},{"family":"Swails","given":"Jason?M."},{"family":"Hernández","given":"Carlos?X."},{"family":"Schwantes","given":"Christian?R."},{"family":"Wang","given":"Lee-Ping"},{"family":"Lane","given":"Thomas?J."},{"family":"Pande","given":"Vijay?S."}],"issued":{"date-parts":[["2015",10,20]]}}}],"schema":""} 21 Molecular models used, regions chosen and molecular simulation protocols are further described below.Preparation of molecular models The following 17 PDB ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"109p6hcfjg","properties":{"formattedCitation":"{\\rtf \\super 22\\nosupersub{}}","plainCitation":"22"},"citationItems":[{"id":261,"uris":[""],"uri":[""],"itemData":{"id":261,"type":"article-journal","title":"The protein data bank: A computer-based archival file for macromolecular structures","container-title":"Archives of Biochemistry and Biophysics","page":"584-591","volume":"185","issue":"2","source":"ScienceDirect","abstract":"The Protein Data Bank is a computer-based archival file for macromolecular structures. The Bank stores in a uniform format atomic co-ordinates and partial bond connectivities, as derived from crystallographic studies. Text included in each data entry gives pertinent information for the structure at hand (e.g. species from which the molecule has been obtained, resolution of diffraction data, literature citations and specifications of secondary structure). In addition to atomic co-ordinates and connectivities, the Protein Data Bank stores structure factors and phases, although these latter data are not placed in any uniform format. Input of data to the Bank and general maintenance functions are carried out at Brookhaven National Laboratory. All data stored in the Bank are available on magnetic tape for public distribution, from Brookhaven (to laboratories in the Americas), Tokyo (Japan), and Cambridge (Europe and worldwide). A master file is maintained at Brookhaven and duplicate copies are stored in Cambridge and Tokyo. In the future, it is hoped to expand the scope of the Protein Data Bank to make available co-ordinates for standard structural types (e.g. α-helix, RNA double-stranded helix) and representative computer programs of utility in the study and interpretation of macromolecular structures.","DOI":"10.1016/0003-9861(78)90204-7","ISSN":"0003-9861","shortTitle":"The protein data bank","journalAbbreviation":"Archives of Biochemistry and Biophysics","author":[{"family":"Bernstein","given":"Frances C."},{"family":"Koetzle","given":"Thomas F."},{"family":"Williams","given":"Graheme J. B."},{"family":"Meyer","given":"Edgar F."},{"family":"Brice","given":"Michael D."},{"family":"Rodgers","given":"John R."},{"family":"Kennard","given":"Olga"},{"family":"Shimanouchi","given":"Takehiko"},{"family":"Tasumi","given":"Mitsuo"}],"issued":{"date-parts":[["1978",1,30]]}}}],"schema":""} 22 structures were used: 4COX ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"q8beeif7d","properties":{"formattedCitation":"{\\rtf \\super 23\\nosupersub{}}","plainCitation":"23"},"citationItems":[{"id":263,"uris":[""],"uri":[""],"itemData":{"id":263,"type":"article-journal","title":"Structural basis for selective inhibition of cyclooxygenase-2 by anti-inflammatory agents","container-title":"Nature","page":"644-648","volume":"384","issue":"6610","source":"","DOI":"10.1038/384644a0","journalAbbreviation":"Nature","language":"en","author":[{"family":"Kurumbail","given":"Ravi G."},{"family":"Stevens","given":"Anna M."},{"family":"Gierse","given":"James K."},{"family":"McDonald","given":"Joseph J."},{"family":"Stegeman","given":"Roderick A."},{"family":"Pak","given":"Jina Y."},{"family":"Gildehaus","given":"Daniel"},{"family":"Iyashiro","given":"Julie M."},{"family":"Penning","given":"Thomas D."},{"family":"Seibert","given":"Karen"},{"family":"Isakson","given":"Peter C."},{"family":"Stallings","given":"William C."}],"issued":{"date-parts":[["1996",12,26]]}}}],"schema":""} 23, 1BMQ ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"tq229s2kk","properties":{"formattedCitation":"{\\rtf \\super 24\\nosupersub{}}","plainCitation":"24"},"citationItems":[{"id":266,"uris":[""],"uri":[""],"itemData":{"id":266,"type":"article-journal","title":"Peptide based interleukin-1 beta converting enzyme (ICE) inhibitors: synthesis, structure activity relationships and crystallographic study of the ICE-inhibitor complex","container-title":"Chemical & Pharmaceutical Bulletin","page":"11-21","volume":"47","issue":"1","source":"PubMed","abstract":"Based on the X-ray structure of the complex of Ac-Tyr-Val-Ala-Asp-H (L-709049) and interleukin-1 beta converting enzyme (ICE), we synthesized compounds which were derived from 2-NapCO-Val-Pro-Asp-CH2OPh (1) to obtain a potent inhibitor in the cell assay. Among these compounds, (3S)-N-methanesulfonyl-3-[[1-[N-(2-naphthoyl)-L-valyl]-L-prolyl]amino]- 4-oxobutanamide (27c) showed high potency not only in the enzyme assay but also cell assay with IC50 values of 38 nM and 0.23 microM, respectively. Compound 27c, with a c log P value of 1.76, had a more hydrophilic character compared with 1. Compound 27c also dose dependently inhibited LPS-primed ATP-induced IL-1 beta release in mice. The crystal structure of the complex of compound 27c and ICE revealed that compound 27c had further interactions with ICE in the naphthoyl group at the P4 position and in the methyl group of the methanesulfonamidecarbonyl group at the P1 position, compared with L-709049. To our knowledge, compound 27c is the first example that shows a strong inhibitory activity without the carboxyl group at the P1 position.","ISSN":"0009-2363","note":"PMID: 9987822","shortTitle":"Peptide based interleukin-1 beta converting enzyme (ICE) inhibitors","journalAbbreviation":"Chem. Pharm. Bull.","language":"eng","author":[{"family":"Okamoto","given":"Y."},{"family":"Anan","given":"H."},{"family":"Nakai","given":"E."},{"family":"Morihira","given":"K."},{"family":"Yonetoku","given":"Y."},{"family":"Kurihara","given":"H."},{"family":"Sakashita","given":"H."},{"family":"Terai","given":"Y."},{"family":"Takeuchi","given":"M."},{"family":"Shibanuma","given":"T."},{"family":"Isomura","given":"Y."}],"issued":{"date-parts":[["1999",1]]},"PMID":"9987822"}}],"schema":""} 24, 1E1X ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2f1qavdn03","properties":{"formattedCitation":"{\\rtf \\super 25\\nosupersub{}}","plainCitation":"25"},"citationItems":[{"id":268,"uris":[""],"uri":[""],"itemData":{"id":268,"type":"article-journal","title":"Identification of Novel Purine and Pyrimidine Cyclin-Dependent Kinase Inhibitors with Distinct Molecular Interactions and Tumor Cell Growth Inhibition Profiles","container-title":"Journal of Medicinal Chemistry","page":"2797-2804","volume":"43","issue":"15","source":"ACS Publications","abstract":"Substituted guanines and pyrimidines were tested as inhibitors of cyclin B1/CDK1 and cyclin A3/CDK2 and soaked into crystals of monomeric CDK2. O6-Cyclohexylmethylguanine (NU2058) was a competitive inhibitor of CDK1 and CDK2 with respect to ATP (Ki values:? CDK1, 5 ± 1 μM; CDK2, 12 ± 3 μM) and formed a triplet of hydrogen bonds (i.e., NH-9 to Glu 81, N-3 to Leu 83, and 2-NH2 to Leu 83). The triplet of hydrogen bonding and CDK inhibition was reproduced by 2,6-diamino-4-cyclohexylmethyloxy-5-nitrosopyrimidine (NU6027, Ki values:? CDK1, 2.5 ± 0.4 μM; CDK2, 1.3 ± 0.2 μM). Against human tumor cells, NU2058 and NU6027 were growth inhibitory in vitro (mean GI50 values of 13 ± 7 μM and 10 ± 6 μM, respectively), with a pattern of sensitivity distinct from flavopiridol and olomoucine. These CDK inhibition and chemosensitivity data indicate that the distinct mode of binding of NU2058 and NU6027 has direct consequences for enzyme and cell growth inhibition.","DOI":"10.1021/jm990628o","ISSN":"0022-2623","journalAbbreviation":"J. Med. Chem.","author":[{"family":"Arris","given":"Christine E."},{"family":"Boyle","given":"F. Thomas"},{"family":"Calvert","given":"A. Hilary"},{"family":"Curtin","given":"Nicola J."},{"family":"Endicott","given":"Jane A."},{"family":"Garman","given":"Elspeth F."},{"family":"Gibson","given":"Ashleigh E."},{"family":"Golding","given":"Bernard T."},{"family":"Grant","given":"Sharon"},{"family":"Griffin","given":"Roger J."},{"family":"Jewsbury","given":"Philip"},{"family":"Johnson","given":"Louise N."},{"family":"Lawrie","given":"Alison M."},{"family":"Newell","given":"David R."},{"family":"Noble","given":"Martin E. M."},{"family":"Sausville","given":"Edward A."},{"family":"Schultz","given":"Robert"},{"family":"Yu","given":"Wyatt"}],"issued":{"date-parts":[["2000",7,1]]}}}],"schema":""} 25, 1E9X ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2h7q3rpuc5","properties":{"formattedCitation":"{\\rtf \\super 26\\nosupersub{}}","plainCitation":"26"},"citationItems":[{"id":271,"uris":[""],"uri":[""],"itemData":{"id":271,"type":"article-journal","title":"Crystal structure of cytochrome P450 14α-sterol demethylase (CYP51) from Mycobacterium tuberculosis in complex with azole inhibitors","container-title":"Proceedings of the National Academy of Sciences","page":"3068-3073","volume":"98","issue":"6","source":"","abstract":"Cytochrome P450 14α-sterol demethylases (CYP51) are essential enzymes in sterol biosynthesis in eukaryotes. CYP51 removes the 14α-methyl group from sterol precursors such as lanosterol, obtusifoliol, dihydrolanosterol, and 24(28)-methylene-24,25-dihydrolanosterol. Inhibitors of CYP51 include triazole antifungal agents fluconazole and itraconazole, drugs used in treatment of topical and systemic mycoses. The 2.1- and 2.2-? crystal structures reported here for 4-phenylimidazole- and fluconazole-bound CYP51 from Mycobacterium tuberculosis (MTCYP51) are the first structures of an authentic P450 drug target. MTCYP51 exhibits the P450 fold with the exception of two striking differences—a bent I helix and an open conformation of BC loop—that define an active site-access channel running along the heme plane perpendicular to the direction observed for the substrate entry in P450BM3. Although a channel analogous to that in P450BM3 is evident also in MTCYP51, it is not open at the surface. The presence of two different channels, with one being open to the surface, suggests the possibility of conformationally regulated substrate-in/product-out openings in CYP51. Mapping mutations identified in Candida albicans azole-resistant isolates indicates that azole resistance in fungi develops in protein regions involved in orchestrating passage of CYP51 through different conformational stages along the catalytic cycle rather than in residues directly contacting fluconazole. These new structures provide a basis for rational design of new, more efficacious antifungal agents as well as insight into the molecular mechanism of P450 catalysis.","DOI":"10.1073/pnas.061562898","ISSN":"0027-8424, 1091-6490","note":"PMID: 11248033","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Podust","given":"Larissa M."},{"family":"Poulos","given":"Thomas L."},{"family":"Waterman","given":"Michael R."}],"issued":{"date-parts":[["2001",3,13]]},"PMID":"11248033"}}],"schema":""} 26, 1E66 ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"op1tb4dbf","properties":{"formattedCitation":"{\\rtf \\super 27\\nosupersub{}}","plainCitation":"27"},"citationItems":[{"id":275,"uris":[""],"uri":[""],"itemData":{"id":275,"type":"article-journal","title":"3D Structure of Torpedo californica Acetylcholinesterase Complexed with Huprine X at 2.1 ? Resolution:? Kinetic and Molecular Dynamic Correlates,","container-title":"Biochemistry","page":"2970-2981","volume":"41","issue":"9","source":"ACS Publications","abstract":"Huprine X is a novel acetylcholinesterase (AChE) inhibitor, with one of the highest affinities reported for a reversible inhibitor. It is a synthetic hybrid that contains the 4-aminoquinoline substructure of one anti-Alzheimer drug, tacrine, and a carbobicyclic moiety resembling that of another AChE inhibitor, (?)-huperzine A. Cocrystallization of huprine X with Torpedo californica AChE yielded crystals whose 3D structure was determined to 2.1 ? resolution. The inhibitor binds to the anionic site and also hinders access to the esteratic site. Its aromatic portion occupies the same binding site as tacrine, stacking between the aromatic rings of Trp84 and Phe330, whereas the carbobicyclic unit occupies the same binding pocket as (?)-huperzine A. Its chlorine substituent was found to lie in a hydrophobic pocket interacting with rings of the aromatic residues Trp432 and Phe330 and with the methyl groups of Met436 and Ile439. Steady-state inhibition data show that huprine X binds to human AChE and Torpedo AChE 28- and 54-fold, respectively, more tightly than tacrine. This difference stems from the fact that the aminoquinoline moiety of huprine X makes interactions similar to those made by tacrine, but additional bonds to the enzyme are made by the huperzine-like substructure and the chlorine atom. Furthermore, both tacrine and huprine X bind more tightly to Torpedo than to human AChE, suggesting that their quinoline substructures interact better with Phe330 than with Tyr337, the corresponding residue in the human AChE structure. Both (?)-huperzine A and huprine X display slow binding properties, but only binding of the former causes a peptide flip of Gly117.","DOI":"10.1021/bi011652i","ISSN":"0006-2960","shortTitle":"3D Structure of Torpedo californica Acetylcholinesterase Complexed with Huprine X at 2.1 ? Resolution","journalAbbreviation":"Biochemistry","author":[{"family":"Dvir","given":"H."},{"family":"Wong","given":"D. M."},{"family":"Harel","given":"M."},{"family":"Barril","given":"X."},{"family":"Orozco","given":"M."},{"family":"Luque","given":"F. J."},{"family":"Mu?oz-Torrero","given":"D."},{"family":"Camps","given":"P."},{"family":"Rosenberry","given":"T. L."},{"family":"Silman","given":"I."},{"family":"Sussman","given":"J. L."}],"issued":{"date-parts":[["2002",3,1]]}}}],"schema":""} 27, 1EZQ ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"4e6uj94h6","properties":{"formattedCitation":"{\\rtf \\super 28\\nosupersub{}}","plainCitation":"28"},"citationItems":[{"id":278,"uris":[""],"uri":[""],"itemData":{"id":278,"type":"article-journal","title":"Crystal Structures of Human Factor Xa Complexed with Potent Inhibitors","container-title":"Journal of Medicinal Chemistry","page":"3226-3232","volume":"43","issue":"17","source":"ACS Publications","abstract":"Involved in the coagulation cascade, factor Xa (FXa) is a serine protease which has received great interest as a potential target for the development of new antithrombotics. Although there is a great wealth of structural data on thrombin complexes, few structures of ligand/FXa complexes have been reported, presumably because of the difficulty in growing crystals. Reproducible crystallization conditions for human des-Gla1?45 coagulation FXa have been found. This has led to an improvement in the diffraction quality of the crystals (about 2.1 ?) when compared to the previously reported forms (2.3?2.8 ?) thus providing a suitable platform for a structure-based drug design approach. A series of crystal structures of noncovalent inhibitors complexed with FXa have been determined, three of which are presented herein. These include compounds containing the benzamidine moiety and surrogates of the basic group. The benzamidine-containing compound binds in a canonical fashion typical of synthetic serine protease inhibitors. On the contrary, molecules that contain surrogates of the benzamidine group do not make direct hydrogen-bonding interactions with the carboxylate of Asp189 at the bottom of the S1 pocket. The structural data provide a likely explanation for the specificity of these inhibitors and a great aid in the design of bioavailable potent FXa inhibitors.","DOI":"10.1021/jm000940u","ISSN":"0022-2623","journalAbbreviation":"J. Med. Chem.","author":[{"family":"Maignan","given":"Sébastien"},{"family":"Guilloteau","given":"Jean-Pierre"},{"family":"Pouzieux","given":"Stéphanie"},{"family":"Choi-Sledeski","given":"Yong Mi"},{"family":"Becker","given":"Michael R."},{"family":"Klein","given":"Scott I."},{"family":"Ewing","given":"William R."},{"family":"Pauls","given":"Henry W."},{"family":"Spada","given":"Alfred P."},{"family":"Mikol","given":"Vincent"}],"issued":{"date-parts":[["2000",8,1]]}}}],"schema":""} 28, 1HWL ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1p8dcb4seq","properties":{"formattedCitation":"{\\rtf \\super 29\\nosupersub{}}","plainCitation":"29"},"citationItems":[{"id":281,"uris":[""],"uri":[""],"itemData":{"id":281,"type":"article-journal","title":"Structural Mechanism for Statin Inhibition of HMG-CoA Reductase","container-title":"Science","page":"1160-1164","volume":"292","issue":"5519","source":"science.","abstract":"HMG-CoA (3-hydroxy-3-methylglutaryl–coenzyme A) reductase (HMGR) catalyzes the committed step in cholesterol biosynthesis. Statins are HMGR inhibitors with inhibition constant values in the nanomolar range that effectively lower serum cholesterol levels and are widely prescribed in the treatment of hypercholesterolemia. We have determined structures of the catalytic portion of human HMGR complexed with six different statins. The statins occupy a portion of the binding site of HMG-CoA, thus blocking access of this substrate to the active site. Near the carboxyl terminus of HMGR, several catalytically relevant residues are disordered in the enzyme-statin complexes. If these residues were not flexible, they would sterically hinder statin binding.","DOI":"10.1126/science.1059344","ISSN":"0036-8075, 1095-9203","note":"PMID: 11349148","language":"en","author":[{"family":"Istvan","given":"Eva S."},{"family":"Deisenhofer","given":"Johann"}],"issued":{"date-parts":[["2001",5,11]]},"PMID":"11349148"}}],"schema":""} 29, 1HWR ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2ps3n29hsf","properties":{"formattedCitation":"{\\rtf \\super 30\\nosupersub{}}","plainCitation":"30"},"citationItems":[{"id":285,"uris":[""],"uri":[""],"itemData":{"id":285,"type":"article-journal","title":"Molecular Recognition of Cyclic Urea HIV-1 Protease Inhibitors","container-title":"Journal of Biological Chemistry","page":"12325-12331","volume":"273","issue":"20","source":"","abstract":"As long as the threat of human immunodeficiency virus (HIV) protease drug resistance still exists, there will be a need for more potent antiretroviral agents. We have therefore determined the crystal structures of HIV-1 protease in complex with six cyclic urea inhibitors: XK216, XK263, DMP323, DMP450, XV638, and SD146, in an attempt to identify 1) the key interactions responsible for their high potency and 2) new interactions that might improve their therapeutic benefit. The structures reveal that the preorganized, C2 symmetric scaffolds of the inhibitors are anchored in the active site of the protease by six hydrogen bonds and that their P1 and P2 substituents participate in extensive van der Waals interactions and hydrogen bonds. Because all of our inhibitors possess benzyl groups at P1 and P1′, their relative binding affinities are modulated by the extent of their P2 interactions, e.g.XK216, the least potent inhibitor (K i (inhibition constant) = 4.70 nm), possesses the smallest P2 and the lowest number of P2-S2 interactions; whereas SD146, the most potent inhibitor (K i = 0.02 nm), contains a benzimidazolylbenzamide at P2 and participates in fourteen hydrogen bonds and ～200 van der Waals interactions. This analysis identifies the strongest interactions between the protease and the inhibitors, suggests ways to improve potency by building into the S2 subsite, and reveals how conformational changes and unique features of the viral protease increase the binding affinity of HIV protease inhibitors.","DOI":"10.1074/jbc.273.20.12325","ISSN":"0021-9258, 1083-351X","journalAbbreviation":"J. Biol. Chem.","language":"en","author":[{"family":"Ala","given":"Paul J."},{"family":"DeLoskey","given":"Richard J."},{"family":"Huston","given":"Edward E."},{"family":"Jadhav","given":"Prabhakar K."},{"family":"Lam","given":"Patrick Y. S."},{"family":"Eyermann","given":"Charles J."},{"family":"Hodge","given":"C. Nicholas"},{"family":"Schadt","given":"Margaret C."},{"family":"Lewandowski","given":"Frank A."},{"family":"Weber","given":"Patricia C."},{"family":"McCabe","given":"Denise D."},{"family":"Duke","given":"Jodie L."},{"family":"Chang","given":"Chong-Hwan"}],"issued":{"date-parts":[["1998",5,15]]}}}],"schema":""} 30, 1IEP ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"r0ah0cou","properties":{"formattedCitation":"{\\rtf \\super 31\\nosupersub{}}","plainCitation":"31"},"citationItems":[{"id":290,"uris":[""],"uri":[""],"itemData":{"id":290,"type":"article-journal","title":"Crystal Structures of the Kinase Domain of c-Abl in Complex with the Small Molecule Inhibitors PD173955 and Imatinib (STI-571)","container-title":"Cancer Research","page":"4236-4243","volume":"62","issue":"15","source":"cancerres.","abstract":"The inadvertent fusion of the bcr gene with the abl gene results in a constitutively active tyrosine kinase (Bcr-Abl) that transforms cells and causes chronic myelogenous leukemia. Small molecule inhibitors of Bcr-Abl that bind to the kinase domain can be used to treat chronic myelogenous leukemia. We report crystal structures of the kinase domain of Abl in complex with two such inhibitors, imatinib (also known as STI-571 and Gleevec) and PD173955 (Parke-Davis). Both compounds bind to the canonical ATP-binding site of the kinase domain, but they do so in different ways. As shown previously in a crystal structure of Abl bound to a smaller variant of STI-571, STI-571 captures a specific inactive conformation of the activation loop of Abl in which the loop mimics bound peptide substrate. In contrast, PD173955 binds to a conformation of Abl in which the activation loop resembles that of an active kinase. The structure suggests that PD173955 would be insensitive to whether the conformation of the activation loop corresponds to active kinases or to that seen in the STI-571 complex. In vitro kinase assays confirm that this is the case and indicate that PD173955 is at least 10-fold more inhibitory than STI-571. The structures suggest that PD173955 achieves its greater potency over STI-571 by being able to target multiple forms of Abl (active or inactive), whereas STI-571 requires a specific inactive conformation of Abl.","ISSN":"0008-5472, 1538-7445","note":"PMID: 12154025","journalAbbreviation":"Cancer Res","language":"en","author":[{"family":"Nagar","given":"Bhushan"},{"family":"Bornmann","given":"William G."},{"family":"Pellicena","given":"Patricia"},{"family":"Schindler","given":"Thomas"},{"family":"Veach","given":"Darren R."},{"family":"Miller","given":"W. Todd"},{"family":"Clarkson","given":"Bayard"},{"family":"Kuriyan","given":"John"}],"issued":{"date-parts":[["2002",8,1]]},"PMID":"12154025"}}],"schema":""} 31, 1KV1 ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"17bth1io16","properties":{"formattedCitation":"{\\rtf \\super 32\\nosupersub{}}","plainCitation":"32"},"citationItems":[{"id":294,"uris":[""],"uri":[""],"itemData":{"id":294,"type":"article-journal","title":"Inhibition of p38 MAP kinase by utilizing a novel allosteric binding site","container-title":"Nature Structural & Molecular Biology","page":"268-272","volume":"9","issue":"4","source":"","abstract":"The p38 MAP kinase plays a crucial role in regulating the production of proinflammatory cytokines, such as tumor necrosis factor and interleukin-1. Blocking this kinase may offer an effective therapy for treating many inflammatory diseases. Here we report a new allosteric binding site for a diaryl urea class of highly potent and selective inhibitors against human p38 MAP kinase. The formation of this binding site requires a large conformational change not observed previously for any of the protein Ser/Thr kinases. This change is in the highly conserved Asp-Phe-Gly motif within the active site of the kinase. Solution studies demonstrate that this class of compounds has slow binding kinetics, consistent with the requirement for conformational change. Improving interactions in this allosteric pocket, as well as establishing binding interactions in the ATP pocket, enhanced the affinity of the inhibitors by 12,000-fold. One of the most potent compounds in this series, BIRB 796, has picomolar affinity for the kinase and low nanomolar inhibitory activity in cell culture.","DOI":"10.1038/nsb770","ISSN":"1072-8368","journalAbbreviation":"Nat Struct Mol Biol","language":"en","author":[{"family":"Pargellis","given":"Christopher"},{"family":"Tong","given":"Liang"},{"family":"Churchill","given":"Laurie"},{"family":"Cirillo","given":"Pier F."},{"family":"Gilmore","given":"Thomas"},{"family":"Graham","given":"Anne G."},{"family":"Grob","given":"Peter M."},{"family":"Hickey","given":"Eugene R."},{"family":"Moss","given":"Neil"},{"family":"Pav","given":"Susan"},{"family":"Regan","given":"John"}],"issued":{"date-parts":[["2002",4]]}}}],"schema":""} 32, 1M17 ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"qc8e3c9fh","properties":{"formattedCitation":"{\\rtf \\super 33\\nosupersub{}}","plainCitation":"33"},"citationItems":[{"id":297,"uris":[""],"uri":[""],"itemData":{"id":297,"type":"article-journal","title":"Structure of the Epidermal Growth Factor Receptor Kinase Domain Alone and in Complex with a 4-Anilinoquinazoline Inhibitor","container-title":"Journal of Biological Chemistry","page":"46265-46272","volume":"277","issue":"48","source":"","abstract":"The crystal structure of the kinase domain from the epidermal growth factor receptor (EGFRK) including forty amino acids from the carboxyl-terminal tail has been determined to 2.6-? resolution, both with and without an EGFRK-specific inhibitor currently in Phase III clinical trials as an anti-cancer agent, erlotinib (OSI-774, CP-358,774, TarcevaTM). The EGFR family members are distinguished from all other known receptor tyrosine kinases in possessing constitutive kinase activity without a phosphorylation event within their kinase domains. Despite its lack of phosphorylation, we find that the EGFRK activation loop adopts a conformation similar to that of the phosphorylated active form of the kinase domain from the insulin receptor. Surprisingly, key residues of a putative dimerization motif lying between the EGFRK domain and carboxyl-terminal substrate docking sites are found in close contact with the kinase domain. Significant intermolecular contacts involving the carboxyl-terminal tail are discussed with respect to receptor oligomerization.","DOI":"10.1074/jbc.M207135200","ISSN":"0021-9258, 1083-351X","note":"PMID: 12196540","journalAbbreviation":"J. Biol. Chem.","language":"en","author":[{"family":"Stamos","given":"Jennifer"},{"family":"Sliwkowski","given":"Mark X."},{"family":"Eigenbrot","given":"Charles"}],"issued":{"date-parts":[["2002",11,29]]},"PMID":"12196540"}}],"schema":""} 33, 1NLJ ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1bc043v30m","properties":{"formattedCitation":"{\\rtf \\super 34\\nosupersub{}}","plainCitation":"34"},"citationItems":[{"id":301,"uris":[""],"uri":[""],"itemData":{"id":301,"type":"article-journal","title":"Azepanone-Based Inhibitors of Human and Rat Cathepsin K","container-title":"Journal of Medicinal Chemistry","page":"1380-1395","volume":"44","issue":"9","source":"ACS Publications","abstract":"The synthesis, in vitro activities, and pharmacokinetics of a series of azepanone-based inhibitors of the cysteine protease cathepsin K (EC 3.4.22.38) are described. These compounds show improved configurational stability of the C-4 diastereomeric center relative to the previously published five- and six-membered ring ketone-based inhibitor series. Studies in this series have led to the identification of 20, a potent, selective inhibitor of human cathepsin K (Ki = 0.16 nM) as well as 24, a potent inhibitor of both human (Ki = 0.0048 nM) and rat (Ki,app = 4.8 nM) cathepsin K. Small-molecule X-ray crystallographic analysis of 20 established the C-4 S stereochemistry as being critical for potent inhibition and that unbound 20 adopted the expected equatorial conformation for the C-4 substituent. Molecular modeling studies predicted the higher energy axial orientation at C-4 of 20 when bound within the active site of cathepsin K, a feature subsequently confirmed by X-ray crystallography. Pharmacokinetic studies in the rat show 20 to be 42% orally bioavailable. Comparison of the transport of the cyclic and acyclic analogues through CaCo-2 cells suggests that oral bioavailability of the acyclic derivatives is limited by a P-glycoprotein-mediated efflux mechanism. It is concluded that the introduction of a conformational constraint has served the dual purpose of increasing inhibitor potency by locking in a bioactive conformation as well as locking out available conformations which may serve as substrates for enzyme systems that limit oral bioavailability.","DOI":"10.1021/jm000481x","ISSN":"0022-2623","journalAbbreviation":"J. Med. Chem.","author":[{"family":"Marquis","given":"Robert W."},{"family":"Ru","given":"Yu"},{"family":"LoCastro","given":"Steven M."},{"family":"Zeng","given":"Jin"},{"family":"Yamashita","given":"Dennis S."},{"family":"Oh","given":"Hye-Ja"},{"family":"Erhard","given":"Karl F."},{"family":"Davis","given":"Larry D."},{"family":"Tomaszek","given":"Thaddeus A."},{"family":"Tew","given":"David"},{"family":"Salyers","given":"Kevin"},{"family":"Proksch","given":"Joel"},{"family":"Ward","given":"Keith"},{"family":"Smith","given":"Brian"},{"family":"Levy","given":"Mark"},{"family":"Cummings","given":"Maxwell D."},{"family":"Haltiwanger","given":"R. Curtis"},{"family":"Trescher","given":"Gudrun"},{"family":"Wang","given":"Bing"},{"family":"Hemling","given":"Mark E."},{"family":"Quinn","given":"Chad J."},{"family":"Cheng","given":"H-Y."},{"family":"Lin","given":"Fan"},{"family":"Smith","given":"Ward W."},{"family":"Janson","given":"Cheryl A."},{"family":"Zhao","given":"Baoguang"},{"family":"McQueney","given":"Michael S."},{"family":"D'Alessio","given":"Karla"},{"family":"Lee","given":"Chao-Pin"},{"family":"Marzulli","given":"Antonia"},{"family":"Dodds","given":"Robert A."},{"family":"Blake","given":"Simon"},{"family":"James","given":"Ian E."},{"family":"Gress","given":"Catherine J."},{"family":"Bradley","given":"Brian R."},{"family":"Lark","given":"Michael W."},{"family":"Gowen","given":"Maxine"},{"family":"Veber","given":"Daniel F."}],"issued":{"date-parts":[["2001",4,1]]}}}],"schema":""} 34, 1OYN ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2ib0s809f","properties":{"formattedCitation":"{\\rtf \\super 35\\nosupersub{}}","plainCitation":"35"},"citationItems":[{"id":304,"uris":[""],"uri":[""],"itemData":{"id":304,"type":"article-journal","title":"Three-Dimensional Structures of PDE4D in Complex with Roliprams and Implication on Inhibitor Selectivity","container-title":"Structure","page":"865-873","volume":"11","issue":"7","source":"ScienceDirect","abstract":"Selective inhibitors against the 11 families of cyclic nucleotide phosphodiesterases (PDEs) are used to treat various human diseases. How the inhibitors selectively bind the conserved PDE catalytic domains is unknown. The crystal structures of the PDE4D2 catalytic domain in complex with (R)- or (R,S)-rolipram suggest that inhibitor selectivity is determined by the chemical nature of amino acids and subtle conformational changes of the binding pockets. The conformational states of Gln369 in PDE4D2 may play a key role in inhibitor recognition. The corresponding Y329S mutation in PDE7 may lead to loss of the hydrogen bonds between rolipram and Gln369 and is thus a possible reason explaining PDE7's insensitivity to rolipram inhibition. Docking of the PDE5 inhibitor sildenafil into the PDE4 catalytic pocket further helps understand inhibitor selectivity.","DOI":"10.1016/S0969-2126(03)00123-0","ISSN":"0969-2126","journalAbbreviation":"Structure","author":[{"family":"Huai","given":"Qing"},{"family":"Wang","given":"Huanchen"},{"family":"Sun","given":"Yingjie"},{"family":"Kim","given":"Hwa-Young"},{"family":"Liu","given":"Yudong"},{"family":"Ke","given":"Hengming"}],"issued":{"date-parts":[["2003",7]]}}}],"schema":""} 35, 1PTY ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1dpsk3brqi","properties":{"formattedCitation":"{\\rtf \\super 36\\nosupersub{}}","plainCitation":"36"},"citationItems":[{"id":307,"uris":[""],"uri":[""],"itemData":{"id":307,"type":"article-journal","title":"Identification of a second aryl phosphate-binding site in protein-tyrosine phosphatase 1B: A paradigm for inhibitor design","container-title":"Proceedings of the National Academy of Sciences","page":"13420-13425","volume":"94","issue":"25","source":"","abstract":"The structure of the catalytically inactive mutant (C215S) of the human protein-tyrosine phosphatase 1B (PTP1B) has been solved to high resolution in two complexes. In the first, crystals were grown in the presence of bis-(para-phosphophenyl) methane (BPPM), a synthetic high-affinity low-molecular weight nonpeptidic substrate (Km = 16 μM), and the structure was refined to an R-factor of 18.2% at 1.9 ? resolution. In the second, crystals were grown in a saturating concentration of phosphotyrosine (pTyr), and the structure was refined to an R-factor of 18.1% at 1.85 ?. Difference Fourier maps showed that BPPM binds PTP1B in two mutually exclusive modes, one in which it occupies the canonical pTyr-binding site (the active site), and another in which a phosphophenyl moiety interacts with a set of residues not previously observed to bind aryl phosphates. The identification of a second pTyr molecule at the same site in the PTP1B/C215S–pTyr complex confirms that these residues constitute a low-affinity noncatalytic aryl phosphate-binding site. Identification of a second aryl phosphate binding site adjacent to the active site provides a paradigm for the design of tight-binding, highly specific PTP1B inhibitors that can span both the active site and the adjacent noncatalytic site. This design can be achieved by tethering together two small ligands that are individually targeted to the active site and the proximal noncatalytic site.","ISSN":"0027-8424, 1091-6490","note":"PMID: 9391040","shortTitle":"Identification of a second aryl phosphate-binding site in protein-tyrosine phosphatase 1B","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Puius","given":"Yoram A."},{"family":"Zhao","given":"Yu"},{"family":"Sullivan","given":"Michael"},{"family":"Lawrence","given":"David S."},{"family":"Almo","given":"Steven C."},{"family":"Zhang","given":"Zhong-Yin"}],"issued":{"date-parts":[["1997",12,9]]},"PMID":"9391040"}}],"schema":""} 36, 1QMF ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1b75nbs4r8","properties":{"formattedCitation":"{\\rtf \\super 37\\nosupersub{}}","plainCitation":"37"},"citationItems":[{"id":311,"uris":[""],"uri":[""],"itemData":{"id":311,"type":"article-journal","title":"The crystal structure of the penicillin-binding protein 2x from Streptococcus pneumoniae and its acyl-enzyme form: implication in drug resistance1","container-title":"Journal of Molecular Biology","page":"477-485","volume":"299","issue":"2","source":"ScienceDirect","abstract":"Penicillin-binding proteins (PBPs), the primary targets for β-lactam antibiotics, are periplasmic membrane-attached proteins responsible for the construction and maintenance of the bacterial cell wall. Bacteria have developed several mechanisms of resistance, one of which is the mutation of the target enzymes to reduce their affinity for β-lactam antibiotics. Here, we describe the structure of PBP2x from Streptococcus pneumoniae determined to 2.4 ?. In addition, we also describe the PBP2x structure in complex with cefuroxime, a therapeutically relevant antibiotic, at 2.8 ?. Surprisingly, two antibiotic molecules are observed: one as a covalent complex with the active-site serine residue, and a second one between the C-terminal and the transpeptidase domains. The structure of PBP2x reveals an active site similar to those of the class A β-lactamases, albeit with an absence of unambiguous deacylation machinery. The structure highlights a few amino acid residues, namely Thr338, Thr550 and Gln552, which are directly related to the resistance phenomenon.","DOI":"10.1006/jmbi.2000.3740","ISSN":"0022-2836","shortTitle":"The crystal structure of the penicillin-binding protein 2x from Streptococcus pneumoniae and its acyl-enzyme form","journalAbbreviation":"Journal of Molecular Biology","author":[{"family":"Gordon","given":"E."},{"family":"Mouz","given":"N."},{"family":"Duée","given":"E."},{"family":"Dideberg","given":"O."}],"issued":{"date-parts":[["2000",6,2]]}}}],"schema":""} 37, 1UDT ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2a4n0cr89o","properties":{"formattedCitation":"{\\rtf \\super 38\\nosupersub{}}","plainCitation":"38"},"citationItems":[{"id":314,"uris":[""],"uri":[""],"itemData":{"id":314,"type":"article-journal","title":"Structure of the catalytic domain of human phosphodiesterase 5 with bound drug molecules","container-title":"Nature","page":"98-102","volume":"425","issue":"6953","source":"","abstract":"Phosphodiesterases (PDEs) are a superfamily of enzymes that degrade the intracellular second messengers cyclic AMP and cyclic GMP. As essential regulators of cyclic nucleotide signalling with diverse physiological functions, PDEs are drug targets for the treatment of various diseases, including heart failure, depression, asthma, inflammation and erectile dysfunction. Of the 12 PDE gene families, cGMP-specific PDE5 carries out the principal cGMP-hydrolysing activity in human corpus cavernosum tissue. It is well known as the target of sildenafil citrate (Viagra) and other similar drugs for the treatment of erectile dysfunction. Despite the pressing need to develop selective PDE inhibitors as therapeutic drugs, only the cAMP-specific PDE4 structures are currently available. Here we present the three-dimensional structures of the catalytic domain (residues 537–860) of human PDE5 complexed with the three drug molecules sildenafil, tadalafil (Cialis) and vardenafil (Levitra). These structures will provide opportunities to design potent and selective PDE inhibitors with improved pharmacological profiles.","DOI":"10.1038/nature01914","ISSN":"0028-0836","journalAbbreviation":"Nature","language":"en","author":[{"family":"Sung","given":"Byung-Je"},{"family":"Yeon Hwang","given":"Kwang"},{"family":"Ho Jeon","given":"Young"},{"family":"Lee","given":"Jae Il"},{"family":"Heo","given":"Yong-Seok"},{"family":"Hwan Kim","given":"Jin"},{"family":"Moon","given":"Jinho"},{"family":"Min Yoon","given":"Jung"},{"family":"Hyun","given":"Young-Lan"},{"family":"Kim","given":"Eunmi"},{"family":"Jin Eum","given":"Sung"},{"family":"Park","given":"Sam-Yong"},{"family":"Lee","given":"Jie-Oh"},{"family":"Gyu Lee","given":"Tae"},{"family":"Ro","given":"Seonggu"},{"family":"Myung Cho","given":"Joong"}],"issued":{"date-parts":[["2003",9,4]]}}}],"schema":""} 38, and 1YCR ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2d2ii81req","properties":{"formattedCitation":"{\\rtf \\super 39\\nosupersub{}}","plainCitation":"39"},"citationItems":[{"id":317,"uris":[""],"uri":[""],"itemData":{"id":317,"type":"article-journal","title":"Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domain","container-title":"Science","page":"948-953","volume":"274","issue":"5289","source":"ProQuest","abstract":"In certain cancers, MDM2 amplification is a common event and contributes to the inactivation of p53. The crystal structure of the 109-residue amino-terminal domain of MDM2 bound to a 15-residue transactivation domain peptide of p53 revealed that MDM2 has a deep hydrophobic cleft on which the p53 peptide binds as an amphipathic alpha helix.","ISSN":"00368075","language":"English","author":[{"family":"Kussie","given":"Paul H."},{"family":"Gorina","given":"Svetlana"},{"family":"Marechal","given":"Vincent"},{"family":"Elenbaas","given":"Brian"},{"family":"al","given":"et"}],"issued":{"date-parts":[["1996",11,8]]}}}],"schema":""} 39. The structures were obtained from the initial PDB structure after the respective ligands and/or co-solutes were removed (if there were dimers or homodimers only the relevant monomer was used). After any ligands were removed, the software tleap (Amber 11) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2it5pnntol","properties":{"formattedCitation":"{\\rtf \\super 40\\nosupersub{}}","plainCitation":"40"},"citationItems":[{"id":319,"uris":[""],"uri":[""],"itemData":{"id":319,"type":"book","title":"AMBER 11","publisher":"University of California, San Francisco","author":[{"literal":"D.A. Case, T.A. Darden, T.E. Cheatham, III, C.L. Simmerling, J. Wang, R.E. Duke, R."},{"literal":"Luo, R.C. Walker, W. Zhang, K.M. Merz, B. Roberts, B. Wang, S. Hayik, A. Roitberg,"},{"literal":"G. Seabra, I. Kolossváry, K.F. Wong, F. Paesani, J. Vanicek, J. Liu, X. Wu, S.R. Brozell,"},{"literal":"T. Steinbrecher, H. Gohlke, Q. Cai, X. Ye, J. Wang, M.-J. Hsieh, G. Cui, D.R. Roe, D.H."},{"literal":"Mathews, M.G. Seetin, C. Sagui, V. Babin, T. Luchko, S. Gusarov, A. Kovalenko, and"},{"literal":"P.A. Kollman"}],"issued":{"date-parts":[["2010"]]}}}],"schema":""} 40 was used to parameterize the system using the AMBER99SB forcefield, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a1ui7qnao","properties":{"formattedCitation":"{\\rtf \\super 41\\nosupersub{}}","plainCitation":"41"},"citationItems":[{"id":320,"uris":[""],"uri":[""],"itemData":{"id":320,"type":"article-journal","title":"Comparison of multiple Amber force fields and development of improved protein backbone parameters","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"712-725","volume":"65","issue":"3","source":"Wiley Online Library","abstract":"The ff94 force field that is commonly associated with the Amber simulation package is one of the most widely used parameter sets for biomolecular simulation. After a decade of extensive use and testing, limitations in this force field, such as over-stabilization of α-helices, were reported by us and other researchers. This led to a number of attempts to improve these parameters, resulting in a variety of “Amber” force fields and significant difficulty in determining which should be used for a particular application. We show that several of these continue to suffer from inadequate balance between different secondary structure elements. In addition, the approach used in most of these studies neglected to account for the existence in Amber of two sets of backbone φ/ψ dihedral terms. This led to parameter sets that provide unreasonable conformational preferences for glycine. We report here an effort to improve the φ/ψ dihedral terms in the ff99 energy function. Dihedral term parameters are based on fitting the energies of multiple conformations of glycine and alanine tetrapeptides from high level ab initio quantum mechanical calculations. The new parameters for backbone dihedrals replace those in the existing ff99 force field. This parameter set, which we denote ff99SB, achieves a better balance of secondary structure elements as judged by improved distribution of backbone dihedrals for glycine and alanine with respect to PDB survey data. It also accomplishes improved agreement with published experimental data for conformational preferences of short alanine peptides and better accord with experimental NMR relaxation data of test protein systems. Proteins 2006. ? 2006 Wiley-Liss, Inc.","DOI":"10.1002/prot.21123","ISSN":"1097-0134","journalAbbreviation":"Proteins","language":"en","author":[{"family":"Hornak","given":"Viktor"},{"family":"Abel","given":"Robert"},{"family":"Okur","given":"Asim"},{"family":"Strockbine","given":"Bentley"},{"family":"Roitberg","given":"Adrian"},{"family":"Simmerling","given":"Carlos"}],"issued":{"date-parts":[["2006",11,15]]}}}],"schema":""} 41 and the TIP4P-EW water models for the solvent. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"k24ai3kg3","properties":{"formattedCitation":"{\\rtf \\super 42\\nosupersub{}}","plainCitation":"42"},"citationItems":[{"id":323,"uris":[""],"uri":[""],"itemData":{"id":323,"type":"article-journal","title":"Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew","container-title":"The Journal of Chemical Physics","page":"9665-9678","volume":"120","issue":"20","source":"scitation.","abstract":"A re-parameterization of the standard TIP4P water model for use with Ewald techniques is introduced, providing an overall global improvement in water properties relative to several popular nonpolarizable and polarizable water potentials. Using high precision simulations, and careful application of standard analytical corrections, we show that the new TIP4P-Ew potential has a density maximum at ～1 °C, and reproduces experimental bulk-densities and the enthalpy of vaporization, ΔH vap , from ?37.5 to 127 °C at 1 atm with an absolute average error of less than 1%. Structural properties are in very good agreement with x-ray scattering intensities at temperatures between 0 and 77 °C and dynamical properties such as self-diffusion coefficient are in excellent agreement with experiment. The parameterization approach used can be easily generalized to rehabilitate any water force field using available experimental data over a range of thermodynamic points.","DOI":"10.1063/1.1683075","ISSN":"0021-9606, 1089-7690","shortTitle":"Development of an improved four-site water model for biomolecular simulations","author":[{"family":"Horn","given":"Hans W."},{"family":"Swope","given":"William C."},{"family":"Pitera","given":"Jed W."},{"family":"Madura","given":"Jeffry D."},{"family":"Dick","given":"Thomas J."},{"family":"Hura","given":"Greg L."},{"family":"Head-Gordon","given":"Teresa"}],"issued":{"date-parts":[["2004",5,22]]}}}],"schema":""} 42 The system was solvated in a rectangular box whose edges extended at least 11 ? away from the edges of the protein. Where appropriate and in agreement with the experimental data, cysteine pairs were modelled as disulfide bonds. Each solvated protein was first energy minimized and then equilibrated with positional restraints for 1 ns with the sander module before production runs.Molecular dynamics simulation protocolsAll subsequent molecular simulations were produced using the software Sire/OpenMM (SOMD). In this study the software SOMD results from the linking of the general purpose molecular simulation package Sire (revision 1786) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"eviihb7hi","properties":{"formattedCitation":"{\\rtf \\super 20\\nosupersub{}}","plainCitation":"20"},"citationItems":[{"id":153,"uris":[""],"uri":[""],"itemData":{"id":153,"type":"book","title":"Sire Molecular Simulation Framework, Revision 1786","URL":"","author":[{"family":"Woods","given":"Christopher"},{"family":"Michel","given":"Julien"}]}}],"schema":""} 20, with the GPU molecular dynamics library OpenMM (revision 3537) ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"j90gqoq0s","properties":{"formattedCitation":"{\\rtf \\super 43\\nosupersub{}}","plainCitation":"43"},"citationItems":[{"id":326,"uris":[""],"uri":[""],"itemData":{"id":326,"type":"article-journal","title":"OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation","container-title":"Journal of Chemical Theory and Computation","page":"461-469","volume":"9","issue":"1","source":"ACS Publications","abstract":"OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.","DOI":"10.1021/ct300857j","ISSN":"1549-9618","shortTitle":"OpenMM 4","journalAbbreviation":"J. Chem. Theory Comput.","author":[{"family":"Eastman","given":"Peter"},{"family":"Friedrichs","given":"Mark S."},{"family":"Chodera","given":"John D."},{"family":"Radmer","given":"Randall J."},{"family":"Bruns","given":"Christopher M."},{"family":"Ku","given":"Joy P."},{"family":"Beauchamp","given":"Kyle A."},{"family":"Lane","given":"Thomas J."},{"family":"Wang","given":"Lee-Ping"},{"family":"Shukla","given":"Diwakar"},{"family":"Tye","given":"Tony"},{"family":"Houston","given":"Mike"},{"family":"Stich","given":"Timo"},{"family":"Klein","given":"Christoph"},{"family":"Shirts","given":"Michael R."},{"family":"Pande","given":"Vijay S."}],"issued":{"date-parts":[["2013",1,8]]}}}],"schema":""} 43. Simulations were run at a pressure of 1 atm and temperature of 298 K using an atom-based Barker-Watts reaction field non-bonded cutoff of 10 ? for the electrostatic interactions with a dielectric constant set to 78.3, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"ke1i4bcv4","properties":{"formattedCitation":"{\\rtf \\super 44\\nosupersub{}}","plainCitation":"44"},"citationItems":[{"id":329,"uris":[""],"uri":[""],"itemData":{"id":329,"type":"article-journal","title":"A generalized reaction field method for molecular dynamics simulations","container-title":"The Journal of Chemical Physics","page":"5451-5459","volume":"102","issue":"13","source":"scitation.","abstract":"Molecular dynamics simulations of ionic systems require the inclusion of long‐range electrostatic forces. We propose an expression for the long‐range electrostatic forces based on an analytical solution of the Poisson–Boltzmann equation outside a spherical cutoff, which can easily be implemented in molecular simulation programs. An analytical solution of the linearized Poisson–Boltzmann (PB) equation valid in a spherical region is obtained. From this general solution special expressions are derived for evaluating the electrostatic potential and its derivative at the origin of the sphere. These expressions have been implemented for molecular dynamics (MD) simulations, such that the surface of the cutoff sphere around a charged particle is identified with the spherical boundary of the Poisson–Boltzmann problem. The analytical solution of the Poisson–Boltzmann equation is valid for the cutoff sphere and can be used for calculating the reaction field forces on the central charge, assuming a uniform continuum of given ionic strength beyond the cutoff. MD simulations are performed for a periodic system consisting of 2127 SPC water molecules with 40 NaCl ions (1 molar). We compare the structural and dynamical results obtained from MD simulations in which the long range electrostatic interactions are treated differently; using a cutoff radius, using a cutoff radius and a Poisson–Boltzmann generalized reaction field force, and using the Ewald summation. Application of the Poisson–Boltzmann generalized reaction field gives a dramatic improvement of the structure of the solution compared to a simple cutoff treatment, at no extra computational cost.","DOI":"10.1063/1.469273","ISSN":"0021-9606, 1089-7690","author":[{"family":"Tironi","given":"Ilario G."},{"family":"Sperb","given":"René"},{"family":"Smith","given":"Paul E."},{"family":"Gunsteren","given":"Wilfred F.","dropping-particle":"van"}],"issued":{"date-parts":[["1995",4,1]]}}}],"schema":""} 44 and an atom-based non-bonded cutoff of 10 ? for the Lennard-Jones interactions. A velocity-Verlet integrator with a time step of 2 fs was used. Temperature control was achieved with an Andersen thermostat with a coupling constant of 10 ps?1. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"76fp33sol","properties":{"formattedCitation":"{\\rtf \\super 45\\nosupersub{}}","plainCitation":"45"},"citationItems":[{"id":332,"uris":[""],"uri":[""],"itemData":{"id":332,"type":"article-journal","title":"Molecular dynamics simulations at constant pressure and/or temperature","container-title":"The Journal of Chemical Physics","page":"2384-2393","volume":"72","issue":"4","source":"scitation.","abstract":"In the molecular dynamics simulation method for fluids, the equations of motion for a collection of particles in a fixed volume are solved numerically. The energy, volume, and number of particles are constant for a particular simulation, and it is assumed that time averages of properties of the simulated fluid are equal to microcanonical ensemble averages of the same properties. In some situations, it is desirable to perform simulations of a fluid for particular values of temperature and/or pressure or under conditions in which the energy and volume of the fluid can fluctuate. This paper proposes and discusses three methods for performing molecular dynamics simulations under conditions of constant temperature and/or pressure, rather than constant energy and volume. For these three methods, it is shown that time averages of properties of the simulated fluid are equal to averages over the isoenthalpic–isobaric, canonical, and isothermal–isobaric ensembles. Each method is a way of describing the dynamics of a certain number of particles in a volume element of a fluid while taking into account the influence of surrounding particles in changing the energy and/or density of the simulated volume element. The influence of the surroundings is taken into account without introducing unwanted surface effects. Examples of situations where these methods may be useful are discussed.","DOI":"10.1063/1.439486","ISSN":"0021-9606, 1089-7690","author":[{"family":"Andersen","given":"Hans C."}],"issued":{"date-parts":[["1980",2,15]]}}}],"schema":""} 45 Pressure control was implemented via attempted isotropic box edge scaling Monte Carlo moves every 25 time steps. The OpenMM default error tolerance settings were used to constrain the intramolecular degrees of freedom of water molecules. For each protein system one simulation of 50 ns was run with velocities randomly drawn from a Maxwell-Boltzmann distribution. In all simulations a harmonic restraint rp was placed on all heavy atoms of a protein P with a force constant of 10 kcal mol?1 ??2 and reference coordinates taken from the initial structure. The use of restraints influences the computed water thermodynamics and this is made explicit in the notations used in the rest of this manuscript by use of the subscript ‘P(rp)’ instead of ‘X’ in the notations defining computed thermodynamic quantities. Snapshots were saved every 1 ps in a DCD format. The first 1 ns of equilibration was not included in the data averaging.Nautilus analyses The Nautilus post-processing tool was used to generate rectangular grids around the protein. For each protein the grid was placed so that it extends at least 4.0 ? away from the extreme edges of each protein at 1 ? grid density. This cutoff was deemed sufficient to analyses first hydration shell interfacial waters. In Nautilus the grid is defined by specifying a coordinate center (xc,yc,zc) and from this center specifying the maximum and minimum grid positions as (xc±Δx, yc±Δy,zc±Δz). Since all heavy atoms in the protein are restrained, spatial variations in hydration thermodynamics are captured on the 3D grid via simple averaging of the MD trajectories. Various regions around amino-acid side chains, clustered sites, or predicted pockets are also chosen as seen in figure 1. Water binding free energies for these regions are then computed and correlated to other descriptors reported below. Amino-acid environment analysesGCT properties were computed for each type of amino acid from the dataset of 17 proteins. A distance cutoff of 4 ? was used to select a set of grid points near specific amino acids throughout the dataset. Beuming et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"nhp8udosr","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":196,"uris":[""],"uri":[""],"itemData":{"id":196,"type":"article-journal","title":"Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"871-883","volume":"80","issue":"3","source":"CrossRef","DOI":"10.1002/prot.23244","ISSN":"08873585","language":"en","author":[{"family":"Beuming","given":"Thijs"},{"family":"Che","given":"Ye"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Shanmugasundaram","given":"Veerabahu"},{"family":"Sherman","given":"Woody"}],"issued":{"date-parts":[["2012",3]]}}}],"schema":""} 14 used a similar cutoff in their IFST study, but there the cutoff was only used to bin density clustered hydration sites. GCT does not need an a priori definition of hydration sites and water properties over all grid points within the specified cutoff are considered. One advantage is that water behavior is resolved even in spatial regions of low solvent density. The cutoff was applied to select grid points near functional groups rather than entire amino acids. The different groups were: carboxylic acids (aspartates and glutamates), side-chain nitrogen(s) (lysines and arginines), hydroxyl groups (threonine, serine, tyrosine), side-chain amides (glutamine and asparagine), ring atoms (tyrosine, histidine, phenylalanine and tryptophan), non-polar side-chains (leucine, isoleucine, valine, and alanine) including hydrogens in the side-chains. The resulting distributions of water thermodynamic properties for each group were then compared using a Kolmogorov-Smirnov test as provided by the R programming language. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"qua5sav7h","properties":{"formattedCitation":"{\\rtf \\super 46\\nosupersub{}}","plainCitation":"46"},"citationItems":[{"id":335,"uris":[""],"uri":[""],"itemData":{"id":335,"type":"book","title":"R: A Language and Environment for Statistical Computing","publisher":"R Foundation for Statistical Computing","publisher-place":"Vienna, Austria","event-place":"Vienna, Austria","URL":"","author":[{"literal":"R Core Team"}],"issued":{"date-parts":[["2014"]]}}}],"schema":""} 46 This nonparametric statistic measures the likelihood that two sets of samples were derived from the same underlying distribution. The empirical cumulative distribution function Fn(x) is given by equation 13:Fn(x) = 1ni=1nI-∞,xXi, (13)where n observations have been binned by the indicator function I[?∞,x] which is equal to 1 if Xi ≤ x otherwise it is equal to zero. This procedure is repeated for both datasets and then a Kolmogorov-Smirnov statistic Dn is computed with equation 14:Dn=supxFnx-F(x), (14)where supx is the supremum, or lowest upper bound of the set of distances derived from the two empirical cumulative distribution functions. A Dn value of zero signifies no difference in the distribution, whereas a value greater than ca. 0.2 for the datasets analysed here suggests a low probability (p-value < 0.05) that the samples are drawn from the same distribution. P-values between distributions are shown in the supplementary information (fig S1).Density-clustering of hydration sitesDensity clustered sites were calculated by assigning to a cluster the grid point with the highest density. This is not necessary to compute thermodynamic properties with GCT, but useful to analyse regions of high water density. All grid points within a neighbor cutoff of that grid point are then assigned to that same cluster. This procedure is then iterated until all grid points have been assigned to a cluster or until no points with a density above a threshold value remain. Here the neighbor cutoff was set at 1.5 ? (roughly the radius of a water molecule) and a density threshold of at least 1.5× that of bulk water. Analysis of crystallographic hydration sitesHydration sites obtained via density-clustering of MD trajectories were compared with hydration sites observed in X-ray diffracted crystal structures with the following protocol. First, each PDB protein structure (including hydration sites) was aligned on to the simulation frame of reference using all heavy atom backbone atoms. Then a density-clustered grid was produced to obtain clustered sites from simulation data. Finally, for each experimental hydration site the minimum distance to a density-clustered site was parison of pockets and binding sitesIn this analysis the hydration thermodynamic properties of up to the top 10 druggable pockets of a protein structure as predicted by the software fpocket ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"209qse3ts7","properties":{"formattedCitation":"{\\rtf \\super 47\\nosupersub{}}","plainCitation":"47"},"citationItems":[{"id":336,"uris":[""],"uri":[""],"itemData":{"id":336,"type":"article-journal","title":"Fpocket: An open source platform for ligand pocket detection","container-title":"BMC Bioinformatics","page":"168","volume":"10","source":"BioMed Central","abstract":"Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.","DOI":"10.1186/1471-2105-10-168","ISSN":"1471-2105","shortTitle":"Fpocket","journalAbbreviation":"BMC Bioinformatics","author":[{"family":"Le Guilloux","given":"Vincent"},{"family":"Schmidtke","given":"Peter"},{"family":"Tuffery","given":"Pierre"}],"issued":{"date-parts":[["2009"]]}}}],"schema":""} 47 are compared to those of the known binding site. This software detects pockets in proteins by using alpha spheres. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1u1jvofbn5","properties":{"formattedCitation":"{\\rtf \\super 48\\nosupersub{}}","plainCitation":"48"},"citationItems":[{"id":340,"uris":[""],"uri":[""],"itemData":{"id":340,"type":"article-journal","title":"Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design","container-title":"Protein Science","page":"1884-1897","volume":"7","issue":"9","source":"Wiley Online Library","abstract":"Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structure-based ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the volume and area of pockets and cavities; and the area and circumference of mouth openings. CAST analysis of over 100 proteins has been carried out; proteins examined include a set of 51 monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the FK506 binding protein, 30 HIV-1 protease-inhibitor complexes, and a number of small and large protein inhibitors. Medium-sized globular proteins typically have 10-20 pockets/cavities. Most often, binding sites are pockets with 1-2 mouth openings; much less frequently they are cavities. Ligand binding pockets vary widely in size, most within the range 102-103 ?3. Statistical analysis reveals that the number of pockets and cavities is correlated with protein size, but there is no correlation between the size of the protein and the size of binding sites. Most frequently, the largest pocket/cavity is the active site, but there are a number of instructive exceptions. Ligand volume and binding site volume are somewhat correlated when binding site volume is < 700 ?3, but the ligand seldom occupies the entire site. Auxiliary pockets near the active site have been suggested as additional binding surface for designed ligands (Mattos C et al., 1994, Nat Struct Bid 1:55-58). Analysis of elastase-inhibitor complexes suggests that CAST can identify ancillary pockets suitable for recruitment in ligand design strategies. Analysis of the FK506 binding protein, and of compounds developed in SAR by NMR (Shuker SB et al., 1996, Science 274:1531-1534), indicates that CAST pocket computation may provide a priori identification of target proteins for linked-fragment design. CAST analysis of 30 HIV-1 protease-inhibitor complexes shows that the flexible active site pocket can vary over a range of 853-1,566 ?3, and that there are two pockets near or adjoining the active site that may be recruited for ligand design.","DOI":"10.1002/pro.5560070905","ISSN":"1469-896X","shortTitle":"Anatomy of protein pockets and cavities","journalAbbreviation":"Protein Science","language":"en","author":[{"family":"Liang","given":"Jie"},{"family":"Woodward","given":"Clare"},{"family":"Edelsbrunner","given":"Herbert"}],"issued":{"date-parts":[["1998",9,1]]}}}],"schema":""} 48 Alpha spheres are defined as spheres which must contact at least 4 atoms within a cut-off distance from the alpha sphere center. These alpha spheres in turn reflect the local curvature: in a protein, buried pockets tend to be occupied by larger quantities of small radii alpha spheres, the surface is typically composed of larger radii alpha spheres, and intermediate radii usually reflect more exposed binding sites and clefts. The general workflow for this analysis is: 1) Use fpocket to generate up to top 10 druggable pockets for each protein in the dataset. 2) Extract pocket coordinates from fpocket output. 3) Select all Nautilus grid points within 1 ? of any pocket-site coordinates to define a spatial region. 4) Binding thermodynamics for water in this region are computed by grouping cells in the region.This protocol produces for each pocket per-site ?Gw,P(rp)s, ?Hw,P(rp)s,-T?Sw,P(rp)s, values, as well as per-water ?Gw,P(rp)s,w, ?Hw,P(rp)s,w,-T?Sw,P(rp)s,w values, relative density, average number of waters, and the solvent-accessible volume of the pocket. For fifteen of the seventeen structures considered here pockets computed by fpocket overlapped well with the location of a known ligand binding site (one site in 4COX, 1E1X, 1E66, 1E9X, 1EZQ, 1IEP, 1KV1, 1M17, 1NLJ, 1OYN, 1UDT and two binding sites in 1PTY and 1QMF), and these sites were included in the analysis. Comparison of electrostatic potential with binding enthalpiesThe goal here was to establish whether the magnitude of the electrostatic potential at particular region of space correlates with the GCT computed enthalpies. To enable a reasonable comparison, only density-clustered sites within 4 ? of the protein obtained from simulations were assessed with Poisson-Boltzmann calculations. This effectively discards regions of space that have high values of the electrostatic potential but are not solvent accessible for steric reasons. The APBS Poisson-Boltzmann solver of Baker et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"20rer9h1bc","properties":{"formattedCitation":"{\\rtf \\super 49\\nosupersub{}}","plainCitation":"49"},"citationItems":[{"id":343,"uris":[""],"uri":[""],"itemData":{"id":343,"type":"article-journal","title":"Electrostatics of nanosystems: Application to microtubules and the ribosome","container-title":"Proceedings of the National Academy of Sciences","page":"10037-10041","volume":"98","issue":"18","source":"","abstract":"Evaluation of the electrostatic properties of biomolecules has become a standard practice in molecular biophysics. Foremost among the models used to elucidate the electrostatic potential is the Poisson-Boltzmann equation; however, existing methods for solving this equation have limited the scope of accurate electrostatic calculations to relatively small biomolecular systems. Here we present the application of numerical methods to enable the trivially parallel solution of the Poisson-Boltzmann equation for supramolecular structures that are orders of magnitude larger in size. As a demonstration of this methodology, electrostatic potentials have been calculated for large microtubule and ribosome structures. The results point to the likely role of electrostatics in a variety of activities of these structures.","DOI":"10.1073/pnas.181342398","ISSN":"0027-8424, 1091-6490","note":"PMID: 11517324","shortTitle":"Electrostatics of nanosystems","journalAbbreviation":"PNAS","language":"en","author":[{"family":"Baker","given":"Nathan A."},{"family":"Sept","given":"David"},{"family":"Joseph","given":"Simpson"},{"family":"Holst","given":"Michael J."},{"family":"McCammon","given":"J. Andrew"}],"issued":{"date-parts":[["2001",8,28]]},"PMID":"11517324"}}],"schema":""} 49 was used and the following protocol was used to implement this analysis: 1) Generate a large coarse grid with APBS but specify that the fine grid contains the same spacing and density used for the Nautilus grids; 2) High-density hydration sites are obtained from the GCT density-clustering method discussed previously in the section density-clustering of hydration sites; 3) The magnitude of the electrostatic potential of each APBS grid point that belongs to a given hydration site is computed and averaged; 4) The average magnitudes are compared with the GCT computed binding enthalpy of that hydration site. Results and DiscussionHydration thermodynamics near amino acidsThe average free energies, enthalpies and entropies of binding of waters near each type of side chain are shown in figure 2. Kolmogorov-Smirnov (KS) tests were also used to estimate the likelihood that the observed distributions were drawn from the same underlying distribution. Figure 3A shows an example of a histogram of the sampled distribution for Alanine (histograms of other distributions are shown in the supplementary information figures S2-S3). Figure 3B shows a heatmap of Dn values. Finally differences in average properties between amino acid groups: polar, negatively charged, positively charged, aliphatic, and aromatic types of amino acids are shown in figure 4.Three trends emerge from analysis of Figures 2-4: first, the majority of the free energy changes have a large enthalpic contribution in comparison to the entropy. The second trend observed is that negatively charged side chains stabilize waters significantly more than any other amino acid type. Thirdly, polar, aliphatic and aromatic have similar variations to each other in the range of free energy values which suggest water stability in these regions should be assessed on a case-by-case basis. Negatively charged amino acids show a clear stabilisation of waters with average binding free energies of ?6.99±0.19, and ?6.94±0.14 kcal mol?1 water?1 for aspartate and glutamate respectively (figure 2). These two amino-acids both have similar distributions and this is reflected by the low D values of the KS tests (figure 3B). Next, amino acids that are positively charged were analyzed. Arginine and lysine have more similar free energy distributions while histidine seems to have a broader distribution, which seems to be related to the different protonation states which were grouped together for this analysis. Thus for histidine it is instructive to analyse the results for the delta/epsilon tautomers and the doubly protonated form. The average binding free energies are -4.82±0.94 (delta tautomer), ?4.98±0.54 (epsilon tautomer), and ?8.60±1.71 kcal mol?1 water?1 (doubly protonated) respectively. Thus the particularly broad distribution for histidine is due to the charged tautomer. Polar amino acids were also analyzed and threonine stabilizes water the most, followed by serine and asparagine as shown in figures 2 and 3B. Interestingly, amino-acids containing an amide side-chain (asparagine, glutamine) stabilized waters less well than hydroxyl containing functional groups, and showed also greater differences their distributions according to the KS test shown in figure 3B. On the other hand, aliphatic amino acids show little difference between the free-energy distributions of alanine, leucine or isoleucine but larger variations are seen for methionine as shown in Figure 3B. This could be due to the effect of the sulfur atom on the sidechain that confers a different environment for solvating water molecules. Aromatic amino acids (phenylalanine, tryptophan, and tyrosine) show few differences amongst themselves, which is also reflected by their similar average free energy per water shown in figure 2. Overall, water near negatively charged amino acids are stabilized the most, followed by water near positively charged amino acids. All other types of amino acids do not exhibit significantly different distributions. Next Watermap values obtained from the Beuming et al. work ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"o3j8lktpp","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":196,"uris":[""],"uri":[""],"itemData":{"id":196,"type":"article-journal","title":"Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"871-883","volume":"80","issue":"3","source":"CrossRef","DOI":"10.1002/prot.23244","ISSN":"08873585","language":"en","author":[{"family":"Beuming","given":"Thijs"},{"family":"Che","given":"Ye"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Shanmugasundaram","given":"Veerabahu"},{"family":"Sherman","given":"Woody"}],"issued":{"date-parts":[["2012",3]]}}}],"schema":""} 14 (figure 4) were compared to the present results. In the IFST study of Beuming et al ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2fk8u3qbgf","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":196,"uris":[""],"uri":[""],"itemData":{"id":196,"type":"article-journal","title":"Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"871-883","volume":"80","issue":"3","source":"CrossRef","DOI":"10.1002/prot.23244","ISSN":"08873585","language":"en","author":[{"family":"Beuming","given":"Thijs"},{"family":"Che","given":"Ye"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Shanmugasundaram","given":"Veerabahu"},{"family":"Sherman","given":"Woody"}],"issued":{"date-parts":[["2012",3]]}}}],"schema":""} 14 entropy changes are invariably unfavorable because the theory used assumes that bulk water is uniformly distributed and has thus no correlations, but water-protein interactions always introduce correlations that decrease water entropy. This assumption appears substantial, indeed Beuming et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2fh2u3ql0c","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":196,"uris":[""],"uri":[""],"itemData":{"id":196,"type":"article-journal","title":"Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"871-883","volume":"80","issue":"3","source":"CrossRef","DOI":"10.1002/prot.23244","ISSN":"08873585","language":"en","author":[{"family":"Beuming","given":"Thijs"},{"family":"Che","given":"Ye"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Shanmugasundaram","given":"Veerabahu"},{"family":"Sherman","given":"Woody"}],"issued":{"date-parts":[["2012",3]]}}}],"schema":""} 14 observed ?S entropy losses for water of up to 20.1 cal mol?1 K?1 (corresponding to –T?S = +6 kcal.mol?1) which exceeds the entropy of bulk water (16.7 cal mol?1K?1). Cell theory makes no such assumption because entropy changes are based upon changes in cell parameters calculated for water in the liquid state and at the interface of a protein. This yields entropy changes that are generally unfavorable but of smaller magnitude than those reported by Beuming et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1uccrv6te1","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":196,"uris":[""],"uri":[""],"itemData":{"id":196,"type":"article-journal","title":"Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"871-883","volume":"80","issue":"3","source":"CrossRef","DOI":"10.1002/prot.23244","ISSN":"08873585","language":"en","author":[{"family":"Beuming","given":"Thijs"},{"family":"Che","given":"Ye"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Shanmugasundaram","given":"Veerabahu"},{"family":"Sherman","given":"Woody"}],"issued":{"date-parts":[["2012",3]]}}}],"schema":""} 14 Average binding enthalpies appear to be more negative in the present GCT analyses than those reported by Beuming et al.14. This may be due to differences in the protocols used to detect and define hydration sites but it is difficult to be certain. The GCT binding enthalpies are in better agreement with those computed by Huggins using IFST for 23 hydration sites. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"m5s3kkng3","properties":{"formattedCitation":"{\\rtf \\super 50\\nosupersub{}}","plainCitation":"50"},"citationItems":[{"id":353,"uris":[""],"uri":[""],"itemData":{"id":353,"type":"article-journal","title":"Quantifying the Entropy of Binding for Water Molecules in Protein Cavities by Computing Correlations","container-title":"Biophysical Journal","page":"928-936","volume":"108","issue":"4","source":"ScienceDirect","abstract":"Protein structural analysis demonstrates that water molecules are commonly found in the internal cavities of proteins. Analysis of experimental data on the entropies of inorganic crystals suggests that the entropic cost of transferring such a water molecule to a protein cavity will not typically be greater than 7.0 cal/mol/K per water molecule, corresponding to a contribution of approximately?+2.0?kcal/mol to the free energy. In this study, we employ the statistical mechanical method of inhomogeneous fluid solvation theory to quantify the enthalpic and entropic contributions of individual water molecules in 19 protein cavities across five different proteins. We utilize information theory to develop a rigorous estimate of the total two-particle entropy, yielding a complete framework to calculate hydration free energies. We show that predictions from inhomogeneous fluid solvation theory are in excellent agreement with predictions from free energy perturbation (FEP) and that these predictions are consistent with experimental estimates. However, the results suggest that water molecules in protein cavities containing charged residues may be subject to entropy changes that contribute more than?+2.0?kcal/mol to the free energy. In all cases, these unfavorable entropy changes are predicted to be dominated by highly favorable enthalpy changes. These findings are relevant to the study of bridging water molecules at protein-protein interfaces as well as in complexes with cognate ligands and small-molecule inhibitors.","DOI":"10.1016/j.bpj.2014.12.035","ISSN":"0006-3495","journalAbbreviation":"Biophysical Journal","author":[{"family":"Huggins","given":"David?J."}],"issued":{"date-parts":[["2015",2,17]]}}}],"schema":""} 50 Huggins reported binding enthalpies ranging from ?18.7 kcal mol-1 to ?3.9 kcal mol-1, with a mean value of ?10.3 kcal mol-1. The mean GCT binding enthalpies reported in Figure 4 are more positive, but this is likely because the present analyses include a very large number of interfacial hydration sites, whereas Huggins study focused on hydration sites found in internal protein cavities. The minimum and maximum computed binding free energies per amino acid in this study (Figures S2-S3, typically ?18 to 0 kcal.mol-1) are indeed consistent with the range of binding free energies computed by Huggins. The range of GCT derived binding enthalpies is also similar to that computed with another IFST implementation in earlier work by Li and Lazaridis for a range of proteins (?19.2 to ?1.4 kcal mol-1). ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"7d0ui7p1a","properties":{"formattedCitation":"{\\rtf \\super 51\\nosupersub{}}","plainCitation":"51"},"citationItems":[{"id":245,"uris":[""],"uri":[""],"itemData":{"id":245,"type":"article-journal","title":"Water at biomolecular binding interfaces","container-title":"Physical Chemistry Chemical Physics","page":"573-581","volume":"9","issue":"5","source":"pubs.","abstract":"Water molecules are often found at the binding interface of biomolecular complexes mediating the interaction between polar groups viahydrogen bonds, or simply filling space providing van der Waals interactions. Recent studies have demonstrated the importance of taking such water molecules into account in docking and binding affinity prediction. Here, we review the recent experimental and theoretical work aimed at quantifying the influence of interfacial water on the thermodynamic properties of binding. We highlight especially our recent results obtained by inhomogeneous fluid solvation theory in several systems and the prediction of the thermodynamic consequences of displacement of the bound water molecule by ligand modification. Finally, we discuss possible directions for further progress in this field.","DOI":"10.1039/B612449F","ISSN":"1463-9084","journalAbbreviation":"Phys. Chem. Chem. Phys.","language":"en","author":[{"family":"Li","given":"Zheng"},{"family":"Lazaridis","given":"Themis"}],"issued":{"date-parts":[["2007",1,22]]}}}],"schema":""} 51 Earlier FEP work from Hamelberg et al. reported binding free energies in the range of ?0.8 to ?3.4 kcal mol-1, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"a1kgaad53","properties":{"formattedCitation":"{\\rtf \\super 52\\nosupersub{}}","plainCitation":"52"},"citationItems":[{"id":411,"uris":[""],"uri":[""],"itemData":{"id":411,"type":"article-journal","title":"Standard Free Energy of Releasing a Localized Water Molecule from the Binding Pockets of Proteins:? Double-Decoupling Method","container-title":"Journal of the American Chemical Society","page":"7683-7689","volume":"126","issue":"24","source":"ACS Publications","abstract":"Localized water molecules in the binding pockets of proteins play an important role in noncovalent association of proteins and small drug compounds. At times, the dominant contribution to the binding free energy comes from the release of localized water molecules in the binding pockets of biomolecules. Therefore, to quantify the energetic importance of these water molecules for drug design purposes, we have used the double-decoupling approach to calculate the standard free energy of tying up a water molecule in the binding pockets of two protein complexes. The double-decoupling approach is based on the underlying principle of statistical thermodynamics. We have calculated the standard free energies of tying up the water molecule in the binding pockets of these complexes to be favorable. These water molecules stabilize the protein?drug complexes by interacting with the ligands and binding pockets. Our results offer ideas that could be used in optimizing protein?drug interactions, by designing ligands that are capable of targeting localized water molecules in protein binding sites. The resulting free energy of ligand binding could benefit from the potential free energy gain accompanying the release of these water molecules. Furthermore, we have examined the theoretical background of the double-decoupling method and its connection to the molecular dynamics thermodynamic integration techniques.","DOI":"10.1021/ja0377908","ISSN":"0002-7863","shortTitle":"Standard Free Energy of Releasing a Localized Water Molecule from the Binding Pockets of Proteins","journalAbbreviation":"J. Am. Chem. Soc.","author":[{"family":"Hamelberg","given":"Donald"},{"family":"McCammon","given":"J. Andrew"}],"issued":{"date-parts":[["2004",6,1]]}}}],"schema":""} 52 whereas Michel et al. reported FEP-derived standard binding free energies for water molecules observed in various X-ray structures in the range of ca. -4 to -11 kcal mol-1.53-54 Overall these figures are also consistent with the range of GCT computed binding free energies. Finally, the negatively charged amino acids in GCT tend to decrease the entropy significantly more than any other amino acid type. However with the IFST implementation of Beuming et al. there is not as large a difference in the per-amino acids variations of the entropy of solvating water molecules. Despite these differences, the overall ranking of the amino acids with both methods follows similar trends.Crystallographic water analysisNext the positions of 1716 crystallographic water sites (derived from all proteins of the dataset except: PDBs 4COX, 1BMQ, 1HWR, 1NLJ and 1YCR which did not contain crystallographic waters) were compared to the position of clusters derived from grid densities computed from molecular dynamics snapshots. Figure 5 shows how the density of MD-derived hydration sites varies as a function of the minimum distance to a hydration site observed in an X-ray diffracted protein crystal. The figure shows that MD-derived hydration sites closer to the crystal water sites tend to have densities greater than bulk water densities, as expected. Conversely, hydration sites further away from a crystal site usually have more bulk-like water densities. There are more sites with bulk-like density at minimum distances of ca. 15-20 ? from a crystallographic water site. These sites are typically not observed in X-ray diffracted structures. As expected this confirms that crystallographic techniques are better suited at discerning denser water sites, rather than low density water sites. Nevertheless, a sizable number of high density hydration sites are also observed far from any crystallographically resolved site. Comparison of Poisson-Boltzmann electrostatic potentials with binding enthalpies A comparison of the binding enthalpies of 29,507 high density sites (density greater than 1.5 times bulk density) within 4 ? of a protein with the computed magnitude of the electrostatic potential at each site is shown in Figure 6. The comparison is done with the average of the magnitude of the electrostatic potential because sites that contain several grid points with high positive or negative values of the electrostatic potential may have a signed average potential close to zero, which does not distinguish them from sites made of grid points with uniformly low values of the electrostatic potential. Comparison with binding free energies would give broadly similar results because variations in binding enthalpies are the dominant contribution to binding free energies (see Figure 9B below). Hydration sites which were found to have a high positive enthalpy of binding (above 2.6 kcal mol-1) all contributing from ?Hw,P(rp)s,w. Further analyses indicated that these sites correspond to regions where a water molecule was sterically hindered and trapped, possibly due to the use of positional restraints on the protein atoms these were removed from subsequent analyses. In general, most sites tend to have a low average magnitude in their electrostatic potential, but this does not imply poor binding enthalpies as evidenced by the wide scatter of binding enthalpies seen in Figure 6. Indeed, any correlation between the enthalpy of binding and the magnitude of the electrostatic potential is very weak. Thus it appears that the stability of a water molecule may not be reliably determined from local values of the electrostatic potential. This is illustrated with some examples taken from this dataset. Figure 7A displays one example with a low magnitude of the electrostatic potential, but large negative enthalpy of binding. The low magnitude of the local electrostatic potential (10.0 kBTec?1) occurs due to cancellation of electric fields induced by a nearby aspartate and two arginine side-chains. The enthalpy of binding is fairly negative (?18.1 kcal mol?1) as a result of good coordination of an oxygen water with two arginine side chain nitrogens, an interaction with an aspartate sidechain oxygen with one of the water’s hydrogens, and finally an interaction with a neighboring water molecule. Figure 7B shows a case of fairly negative enthalpy of binding (?21.8 kcal mol?1) and high magnitude of the average local electrostatic potential (46.1 kBTec?1). The coordination environment of the hydration site is fairly similar to the site in Figure 7A, it involves one aspartate, two arginines and a threonine hydroxyl group. Consequently, the enthalpy of binding is similar, but the electrostatic potential differs significantly. Figure 7C shows an example where the electrostatic potential has high magnitude (59.3 kBTec?1). This occurs because the hydration site is close to a positively charged lysine. The electric field induced by this residue is not offset by neighboring negatively charged side-chains. However, the enthalpy of binding is poor (?1.8 kcal mol?1) because water in this environment is unable to coordinate effectively with the lysine’s ammonium group and can engage in at most one hydrogen-bond with a neighboring water molecule. Taken together Figure 6 and 7 shows that continuum electrostatic calculations may not be reliably used to estimate the stability of a hydration site, and inspection of the coordinating environment is a better indicator of thermodynamic parison of protein pockets with ligand binding sitesFigure 8A depicts the distributions of free energy and enthalpy of binding of water in known binding sites and other pockets found in the dataset. Enthalpy of binding is the largest component of the binding free energy of the pockets. After normalization with respect to the number of water molecules there is no significant difference between the distributions, given the available data (Figure 8A). The mean binding enthalpies are both ?3.1±0.2 kcal.mol-1, and the standard deviations 0.9 and 2.2 kcal.mol-1 for the binding site and pocket datasets respectively. For the entropy of binding the per-water statistics are also comparable (Figure 8B). Finally, Figure 8C shows that binding sites contain a larger number of water molecules than pockets, and this is because the volume of the binding sites is larger (Figure 8D). Taken together, these results suggest that these binding sites do not appear to generate an unusual signature in the computed hydration thermodynamics when average water properties over a complete pocket are considered. Rather the location of ligand binding sites in proteins may inferred by analysis of the solvent-accessible volume of pockets. This findings contrast with reports from Beuming et al.14 or Vukovic et al.55 that developed druggability descriptors based on computed water binding thermodynamics. The main differences with the present work are that these studies focused on detection of the least stable (or clusters of) high-density hydration sites, whereas here average per-water properties over a larger volume of space were considered. Thermodynamic properties of high-density hydration sitesNext high-density hydration sites were analyzed further as these often involve structured water that are important for protein stability and/or function. Most sites have a free energy of binding between 0 to –15 kcal mol?1, with extreme cases reaching up to –50 kcal mol?1 There is only a weak anti-correlation between the entropy and free energy (Figure 9A). Entropies of binding are generally positive (up to ca. 2.5 kcal mol?1) but in some instances negative entropies of binding are observed. Figure 9B shows that by contrast there is a strong correlation between the enthalpy and free energy and this reflects also the large contribution that this component makes to the free energy.Hydration sites with unusual entropies of binding were further inspected. Figure 10A depicts a site with a tightly bound water molecule (?Gw,P(rp)s = –48.5 kcal mol?1) and an unfavorable entropy of binding (-T?Sw,P(rp)s= 2.2 kcal mol?1). This buried hydration site is coordinated by two nearby water molecules, a threonine’s carbonyl oxygen and a glutamate carboxylate. Further electrostatic stabilization is provided by a closely placed aspartate. Motions in this hydration site are highly restricted, hence the unfavorable entropy of binding. Figure 10B depicts a different situation where water in the hydration site is more solvent accessible and connects to bulk. Water at this hydration site interacts with the amide side chain nitrogen atom as well as the amide backbone nitrogen of a glutamine. However, interactions with the carbonyl oxygens of a neighboring histidine and aspartate are also possible. Although water in this hydration site is hindered in its translations, it is able to form hydrogen-bonds in many ways with backbone donor/acceptors and neighboring bulk solvent. Consequently, the entropy of binding is more negative than in bulk water (-T?Sw,P(rp)s= –0.7 kcal mol?1). Next, the components of the enthalpy and entropy of binding were investigated. Figure 11A shows the enthalpies of binding ?Hw,P(rp)s, the water-water component of the enthalpy of binding ?Hws and the water-solute component of the enthalpy of binding ?HP(rp)s. Evaluation of the distributions shows that the water-water enthalpic component tends to be unfavorable, whereas the water-solute enthalpic component is favorable. In more detail water-water enthalpies are above zero for 28.4% of the sites, whereas this never happens with water-solute enthalpies. Figure 11B depicts the components of the entropy of binding. From the plot the percentage of sites in which components contribute favorably or unfavorably to the free energy can be estimated. In this dataset 86.2% of the orientational entropy components are thermodynamically unfavorable, and this is followed by librational entropy (80.4% unfavorable) and vibrational entropy (73.8%). Further insights may be gained by evaluating correlations between the distributions shown in Figure 12. Entropy loss in the protein hydration layer has a maximum of 2.3 kcal mol?1, slightly higher than the experimental limits of 2 kcal mol?1 suggested by Dunitz.56 Figure 12A and 12B show that changes in orientational entropy correlate little with changes in vibrational or librational entropies. Figure 12C shows that there is stronger correlation between changes in vibrational and librational entropy, and the magnitude of changes in vibrational entropy are slightly larger. Finally the correlation between water-solute and water-water components of the enthalpy of binding is shown in Figure 12D. This indicates that water molecules that interact strongly with a protein (low ?HP(rp)svalues) also tend to interact strongly with neighboring water molecules (low ?Hws values).ConclusionsExtensive analysis of hydration sites surrounding 17 proteins has afforded a number of novel insights into binding thermodynamics at protein interfaces. On average water free energies are more negative near acidic amino-acids groups, followed by basic amino-acids, but differences between polar and non-polar amino acids are small. Differences in free energy distributions around each amino-acid were also evaluated, and revealed a broadly similar picture to the analysis of average binding free energies. Qualitatively, these results are similar to those reported by Beuming et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"g03f81am7","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":196,"uris":[""],"uri":[""],"itemData":{"id":196,"type":"article-journal","title":"Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"871-883","volume":"80","issue":"3","source":"CrossRef","DOI":"10.1002/prot.23244","ISSN":"08873585","language":"en","author":[{"family":"Beuming","given":"Thijs"},{"family":"Che","given":"Ye"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Shanmugasundaram","given":"Veerabahu"},{"family":"Sherman","given":"Woody"}],"issued":{"date-parts":[["2012",3]]}}}],"schema":""} 14 that used an IFST implementation for their analyses. However, there is significant variability between the two methods in terms of the magnitude of the computed water enthalpies and entropies. These differences are attributed to the protocol used to define hydration sites, and the different theories used to calculate entropy. This discrepancy in computed water enthalpies of binding also is specific to the work by Beuming et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"ra058vhjb","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":196,"uris":[""],"uri":[""],"itemData":{"id":196,"type":"article-journal","title":"Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization","container-title":"Proteins: Structure, Function, and Bioinformatics","page":"871-883","volume":"80","issue":"3","source":"CrossRef","DOI":"10.1002/prot.23244","ISSN":"08873585","language":"en","author":[{"family":"Beuming","given":"Thijs"},{"family":"Che","given":"Ye"},{"family":"Abel","given":"Robert"},{"family":"Kim","given":"Byungchan"},{"family":"Shanmugasundaram","given":"Veerabahu"},{"family":"Sherman","given":"Woody"}],"issued":{"date-parts":[["2012",3]]}}}],"schema":""} 14 ranges found in other work described before have ranges of similar values. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1sja4a5bls","properties":{"formattedCitation":"{\\rtf \\super 50,51\\nosupersub{}}","plainCitation":"50,51"},"citationItems":[{"id":353,"uris":[""],"uri":[""],"itemData":{"id":353,"type":"article-journal","title":"Quantifying the Entropy of Binding for Water Molecules in Protein Cavities by Computing Correlations","container-title":"Biophysical Journal","page":"928-936","volume":"108","issue":"4","source":"ScienceDirect","abstract":"Protein structural analysis demonstrates that water molecules are commonly found in the internal cavities of proteins. Analysis of experimental data on the entropies of inorganic crystals suggests that the entropic cost of transferring such a water molecule to a protein cavity will not typically be greater than 7.0 cal/mol/K per water molecule, corresponding to a contribution of approximately?+2.0?kcal/mol to the free energy. In this study, we employ the statistical mechanical method of inhomogeneous fluid solvation theory to quantify the enthalpic and entropic contributions of individual water molecules in 19 protein cavities across five different proteins. We utilize information theory to develop a rigorous estimate of the total two-particle entropy, yielding a complete framework to calculate hydration free energies. We show that predictions from inhomogeneous fluid solvation theory are in excellent agreement with predictions from free energy perturbation (FEP) and that these predictions are consistent with experimental estimates. However, the results suggest that water molecules in protein cavities containing charged residues may be subject to entropy changes that contribute more than?+2.0?kcal/mol to the free energy. In all cases, these unfavorable entropy changes are predicted to be dominated by highly favorable enthalpy changes. These findings are relevant to the study of bridging water molecules at protein-protein interfaces as well as in complexes with cognate ligands and small-molecule inhibitors.","DOI":"10.1016/j.bpj.2014.12.035","ISSN":"0006-3495","journalAbbreviation":"Biophysical Journal","author":[{"family":"Huggins","given":"David?J."}],"issued":{"date-parts":[["2015",2,17]]}}},{"id":245,"uris":[""],"uri":[""],"itemData":{"id":245,"type":"article-journal","title":"Water at biomolecular binding interfaces","container-title":"Physical Chemistry Chemical Physics","page":"573-581","volume":"9","issue":"5","source":"pubs.","abstract":"Water molecules are often found at the binding interface of biomolecular complexes mediating the interaction between polar groups viahydrogen bonds, or simply filling space providing van der Waals interactions. Recent studies have demonstrated the importance of taking such water molecules into account in docking and binding affinity prediction. Here, we review the recent experimental and theoretical work aimed at quantifying the influence of interfacial water on the thermodynamic properties of binding. We highlight especially our recent results obtained by inhomogeneous fluid solvation theory in several systems and the prediction of the thermodynamic consequences of displacement of the bound water molecule by ligand modification. Finally, we discuss possible directions for further progress in this field.","DOI":"10.1039/B612449F","ISSN":"1463-9084","journalAbbreviation":"Phys. Chem. Chem. Phys.","language":"en","author":[{"family":"Li","given":"Zheng"},{"family":"Lazaridis","given":"Themis"}],"issued":{"date-parts":[["2007",1,22]]}}}],"schema":""} 50,51 A comparison of density-clustered hydration sites and hydration sites resolved in X-ray diffracted protein structures reveals that the molecular simulations do tend to assign high-density hydration sites near X-ray resolved sites, but detect also many other high and low-density sites. Comparisons of computed enthalpies with Poisson-Boltzmann electrostatic calculations suggest that the stability of a water molecule may not be generally inferred from inspection of the magnitude of the local electrostatic potential. Rather, it is the nature of the coordination environment that must be taken into account. The thermodynamic properties of water in known ligand binding sites do not differ from the thermodynamic properties computed in other pockets. However, since binding sites tend to be made of the largest pocket, they can also be identified by evaluation of the water-accessible volume of a pocket. Lastly, high-density hydration sites are stabilized mostly by the enthalpy of interactions between the protein and water, and in rare occasions entropically stabilized by favorable changes in vibrational and librational entropy. The maximum energetic contribution of an entropy loss of a hydration site at a protein surface to the free energy is around +2.5 kcal mol?1 which correlates well with the estimate of +2 kcal mol?1 originally put forward by Dunitz.56 Future work in this topic could focus on parameterizing simple empirical models that may predict the molecular simulation computed water thermodynamics from rapid structural analysis of a protein. It would also be interesting to repeat similar analyses using more elaborate definitions of the orientational entropy term such as those recently proposed by Henchman and coworkers.57 Finally, as the protein structures studied here were rigid, it would be intriguing to explore how fluctuations in local binding thermodynamics are coupled to protein conformational changes.ASSOCIATED CONTENTSupporting Information. Heatmap of significance values of differences in amino acid water binding free energy distributions; tables of average water binding free energies of amino acids, and amino acid groups, distributions of water binding free energy distributions for each amino acid, distributions of amino acids around hydration sites with density cutoff 10× greater than bulk. This material is available free of charge via the Internet at is supported by a University Research Fellowship from the Royal Society. RHH is supported by BBSRC Grant BB/K001558/1. This research was also supported by EPSRC through an award of a CASE studentship to GG. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Corresponding Author* mail@Present AddressesMax Planck Institute for Coal Research, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der RuhrREFERENCES ADDIN ZOTERO_BIBL {"custom":[]} CSL_BIBLIOGRAPHY (1) England, J. L.; Pande, V. S. Charge, Hydrophobicity, and Confined Water: Putting Past Simulations into a Simple Theoretical framework. Biochem. Cell Biol. 2010, 88, 359–369.(2) Nicholls, A.; Sharp, K. A.; Honig, B. Protein Folding and Association: Insights from the Interfacial and Thermodynamic Properties of Hydrocarbons. Proteins Struct. Funct. Bioinforma. 1991, 11, 281–296.(3) Khandelwal, G.; Jayaram, B. DNA–Water Interactions Distinguish Messenger RNA Genes from Transfer RNA Genes. J. Am. Chem. Soc. 2012, 134, 8814–8816.(4) Spyrakis, F.; Cozzini, P.; Bertoli, C.; Marabotti, A.; Kellogg, G. E.; Mozzarelli, A. Energetics of the Protein-DNA-Water Interaction. BMC Struct. Biol. 2007, 7, 1-18.(5) Reddy, C. K.; Das, A.; Jayaram, B. Do Water Molecules Mediate Protein-DNA recognition?1. J. Mol. Biol. 2001, 314, 619–632.(6) Conte, L. L.; Chothia, C.; Janin, J. The Atomic Structure of Protein-Protein Recognition sites1. J. Mol. Biol. 1999, 285, 2177–2198.(7) Michel, J. Current and Emerging Opportunities for Molecular Simulations in Structure-Based Drug Design. Phys. Chem. Chem. Phys., 2014, 16, 4465-4477.(8) Jungwirth, P. Biological Water or Rather Water in Biology? J. Phys. Chem. Lett. 2015, 6, 2449–2451.(9) Sebastiani, F.; Orecchini, A.; Paciaroni, A.; Jasnin, M.; Zaccai, G.; Moulin, M.; Haertlein, M.; De Francesco, A.; Petrillo, C.; Sacchetti, F. Collective THz Dynamics in Living Escherichia Coli Cells. Chem. Phys. 2013, 424, 84–88.(10) Fogarty, A. C.; Laage, D. Water Dynamics in Protein Hydration Shells: The Molecular Origins of the Dynamical Perturbation. J. Phys. Chem. B 2014, 118, 7715–7729.(11) Martin, D. R.; Matyushov, D. V. Hydration Shells of Proteins Probed by Depolarized Light Scattering and Dielectric Spectroscopy: Orientational Structure Is Significant, Positional Structure Is Not. J. Chem. Phys. 2014, 141, 22D501.(12) Conti Nibali, V.; Havenith, M. New Insights into the Role of Water in Biological Function: Studying Solvated Biomolecules Using Terahertz Absorption Spectroscopy in Conjunction with Molecular Dynamics Simulations. J. Am. Chem. Soc. 2014, 136, 12800–12807.(13) Persson, E.; Halle, B. Cell Water Dynamics on Multiple Time Scales. Proc. Natl. Acad. Sci. 2008, 105, 6266–6271.(14) Beuming, T.; Che, Y.; Abel, R.; Kim, B.; Shanmugasundaram, V.; Sherman, W. Thermodynamic Analysis of Water Molecules at the Surface of Proteins and Applications to Binding Site Prediction and Characterization. Proteins Struct. Funct. Bioinforma. 2012, 80, 871–883.(15) Young, T.; Abel, R.; Kim, B.; Berne, B. J.; Friesner, R. A. Motifs for Molecular Recognition Exploiting Hydrophobic Enclosure in Protein–ligand Binding. Proc. Natl. Acad. Sci. 2007, 104, 808–813.(16) Henchman, R. H. Free Energy of Liquid Water from a Computer Simulation via Cell Theory. J. Chem. Phys. 2007, 126, 064504.(17) Gerogiokas, G.; Calabro, G.; Henchman, R. H.; Southey, M. W. Y.; Law, R. J.; Michel, J. Prediction of Small Molecule Hydration Thermodynamics with Grid Cell Theory. J. Chem. Theory Comput. 2014, 10, 35–48.(18) Michel, J.; Henchman, R. H.; Gerogiokas, G.; Southey, M. W. Y.; Mazanetz, M. P.; Law, R. J. Evaluation of Host–Guest Binding Thermodynamics of Model Cavities with Grid Cell Theory. J. Chem. Theory Comput. 2014, 10, 4055–4068.(19) Gerogiokas, G.; Southey, M. W. Y.; Mazanetz, M. P.; Hefeitz, A.; Bodkin, M.; Law, R. J.; Michel, J. Evaluation of Water Displacement Energetics in Protein Binding Sites with Grid Cell Theory. Phys. Chem. Chem. Phys. 2015, 17, 8416–8426.(20) Woods, C.; Michel, J. Sire Molecular Simulation Framework, Revision 1786, (accessed September 18, 2016).(21) McGibbon, R. T.; Beauchamp, K. A.; Harrigan, M. P.; Klein, C.; Swails, J. M.; Hernández, C. X.; Schwantes, C. R.; Wang, L.-P.; Lane, T. J.; Pande, V. S. MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories. Biophys. J. 2015, 109, 1528–1532.(22) Bernstein, F. C.; Koetzle, T. F.; Williams, G. J. B.; Meyer, E. F.; Brice, M. D.; Rodgers, J. R.; Kennard, O.; Shimanouchi, T.; Tasumi, M. The Protein Data Bank: A Computer-Based Archival File for Macromolecular Structures. Arch. Biochem. Biophys. 1978, 185, 584–591.(23) Kurumbail, R. G.; Stevens, A. M.; Gierse, J. K.; McDonald, J. J.; Stegeman, R. A.; Pak, J. Y.; Gildehaus, D.; Iyashiro, J. M.; Penning, T. D.; Seibert, K.; et al. Structural Basis for Selective Inhibition of Cyclooxygenase-2 by Anti-Inflammatory Agents. Nature 1996, 384, 644–648.(24) Okamoto, Y.; Anan, H.; Nakai, E.; Morihira, K.; Yonetoku, Y.; Kurihara, H.; Sakashita, H.; Terai, Y.; Takeuchi, M.; Shibanuma, T.; et al. Peptide Based Interleukin-1 Beta Converting Enzyme (ICE) Inhibitors: Synthesis, Structure Activity Relationships and Crystallographic Study of the ICE-Inhibitor Complex. Chem. Pharm. Bull. (Tokyo) 1999, 47, 11–21.(25) Arris, C. E.; Boyle, F. T.; Calvert, A. H.; Curtin, N. J.; Endicott, J. A.; Garman, E. F.; Gibson, A. E.; Golding, B. T.; Grant, S.; Griffin, R. J.; et al. Identification of Novel Purine and Pyrimidine Cyclin-Dependent Kinase Inhibitors with Distinct Molecular Interactions and Tumor Cell Growth Inhibition Profiles. J. Med. Chem. 2000, 43, 2797–2804.(26) Podust, L. M.; Poulos, T. L.; Waterman, M. R. Crystal Structure of Cytochrome P450 14α-Sterol Demethylase (CYP51) from Mycobacterium Tuberculosis in Complex with Azole Inhibitors. Proc. Natl. Acad. Sci. 2001, 98, 3068–3073.(27) Dvir, H.; Wong, D. M.; Harel, M.; Barril, X.; Orozco, M.; Luque, F. J.; Mu?oz-Torrero, D.; Camps, P.; Rosenberry, T. L.; Silman, I.; et al. 3D Structure of Torpedo Californica Acetylcholinesterase Complexed with Huprine X at 2.1 ? Resolution:? Kinetic and Molecular Dynamic Correlates,. Biochemistry (Mosc.) 2002, 41, 2970–2981.(28) Maignan, S.; Guilloteau, J.-P.; Pouzieux, S.; Choi-Sledeski, Y. M.; Becker, M. R.; Klein, S. I.; Ewing, W. R.; Pauls, H. W.; Spada, A. P.; Mikol, V. Crystal Structures of Human Factor Xa Complexed with Potent Inhibitors. J. Med. Chem. 2000, 43, 3226–3232.(29) Istvan, E. S.; Deisenhofer, J. Structural Mechanism for Statin Inhibition of HMG-CoA Reductase. Science 2001, 292, 1160–1164.(30) Ala, P. J.; DeLoskey, R. J.; Huston, E. E.; Jadhav, P. K.; Lam, P. Y. S.; Eyermann, C. J.; Hodge, C. N.; Schadt, M. C.; Lewandowski, F. A.; Weber, P. C.; et al. Molecular Recognition of Cyclic Urea HIV-1 Protease Inhibitors. J. Biol. Chem. 1998, 273, 12325–12331.(31) Nagar, B.; Bornmann, W. G.; Pellicena, P.; Schindler, T.; Veach, D. R.; Miller, W. T.; Clarkson, B.; Kuriyan, J. Crystal Structures of the Kinase Domain of c-Abl in Complex with the Small Molecule Inhibitors PD173955 and Imatinib (STI-571). Cancer Res. 2002, 62, 4236–4243.(32) Pargellis, C.; Tong, L.; Churchill, L.; Cirillo, P. F.; Gilmore, T.; Graham, A. G.; Grob, P. M.; Hickey, E. R.; Moss, N.; Pav, S.; et al. Inhibition of p38 MAP Kinase by Utilizing a Novel Allosteric Binding Site. Nat. Struct. Mol. Biol. 2002, 9, 268–272.(33) Stamos, J.; Sliwkowski, M. X.; Eigenbrot, C. Structure of the Epidermal Growth Factor Receptor Kinase Domain Alone and in Complex with a 4-Anilinoquinazoline Inhibitor. J. Biol. Chem. 2002, 277, 46265–46272.(34) Marquis, R. W.; Ru, Y.; LoCastro, S. M.; Zeng, J.; Yamashita, D. S.; Oh, H.-J.; Erhard, K. F.; Davis, L. D.; Tomaszek, T. A.; Tew, D.; et al. Azepanone-Based Inhibitors of Human and Rat Cathepsin K. J. Med. Chem. 2001, 44, 1380–1395.(35) Huai, Q.; Wang, H.; Sun, Y.; Kim, H.-Y.; Liu, Y.; Ke, H. Three-Dimensional Structures of PDE4D in Complex with Roliprams and Implication on Inhibitor Selectivity. Structure 2003, 11, 865–873.(36) Puius, Y. A.; Zhao, Y.; Sullivan, M.; Lawrence, D. S.; Almo, S. C.; Zhang, Z.-Y. Identification of a Second Aryl Phosphate-Binding Site in Protein-Tyrosine Phosphatase 1B: A Paradigm for Inhibitor Design. Proc. Natl. Acad. Sci. 1997, 94, 13420–13425.(37) Gordon, E.; Mouz, N.; Duée, E.; Dideberg, O. The Crystal Structure of the Penicillin-Binding Protein 2x from Streptococcus Pneumoniae and Its Acyl-Enzyme Form: Implication in Drug resistance1. J. Mol. Biol. 2000, 299, 477–485.(38) Sung, B.-J.; Yeon Hwang, K.; Ho Jeon, Y.; Lee, J. I.; Heo, Y.-S.; Hwan Kim, J.; Moon, J.; Min Yoon, J.; Hyun, Y.-L.; Kim, E.; et al. Structure of the Catalytic Domain of Human Phosphodiesterase 5 with Bound Drug Molecules. Nature 2003, 425, 98–102.(39) Kussie, P. H.; Gorina, S.; Marechal, V.; Elenbaas, B.; al, et. Structure of the MDM2 Oncoprotein Bound to the p53 Tumor Suppressor Transactivation Domain. Science 1996, 274, 948–953.(40) D.A. Case, T.A. Darden, T.E. Cheatham, III, C.L. Simmerling, J. Wang, R.E. Duke, R.; Luo, R.C. Walker, W. Zhang, K.M. Merz, B. et al. AMBER 11; University of California, San Francisco, 2010.(41) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of Multiple Amber Force Fields and Development of Improved Protein Backbone Parameters. Proteins Struct. Funct. Bioinforma. 2006, 65, 712–725.(42) Horn, H. W.; Swope, W. C.; Pitera, J. W.; Madura, J. D.; Dick, T. J.; Hura, G. L.; Head-Gordon, T. Development of an Improved Four-Site Water Model for Biomolecular Simulations: TIP4P-Ew. J. Chem. Phys. 2004, 120, 9665–9678.(43) Eastman, P.; Friedrichs, M. S.; Chodera, J. D.; Radmer, R. J.; Bruns, C. M.; Ku, J. P.; Beauchamp, K. A.; Lane, T. J.; Wang, L.-P.; Shukla, D.; et al. OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation. J. Chem. Theory Comput. 2013, 9, 461–469.(44) Tironi, I. G.; Sperb, R.; Smith, P. E.; Gunsteren, W. F. van. A Generalized Reaction Field Method for Molecular Dynamics Simulations. J. Chem. Phys. 1995, 102 (13), 5451–5459.(45) Andersen, H. C. Molecular Dynamics Simulations at Constant Pressure And/or Temperature. J. Chem. Phys. 1980, 72, 2384–2393.(46) R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014.(47) Le Guilloux, V.; Schmidtke, P.; Tuffery, P. Fpocket: An Open Source Platform for Ligand Pocket Detection. BMC Bioinformatics 2009, 10, 168.(48) Liang, J.; Woodward, C.; Edelsbrunner, H. Anatomy of Protein Pockets and Cavities: Measurement of Binding Site Geometry and Implications for Ligand Design. Protein Sci. 1998, 7, 1884–1897.(49) Baker, N. A.; Sept, D.; Joseph, S.; Holst, M. J.; McCammon, J. A. Electrostatics of Nanosystems: Application to Microtubules and the Ribosome. Proc. Natl. Acad. Sci. 2001, 98, 10037–10041.(50) Huggins, D. J. Quantifying the Entropy of Binding for Water Molecules in Protein Cavities by Computing Correlations. Biophys. J. 2015, 108, 928–936.(51) Li, Z.; Lazaridis, T. Water at Biomolecular Binding Interfaces. Phys. Chem. Chem. Phys. 2007, 9, 573–581.(52) Hamelberg, D.; McCammon, J. A. Standard Free Energy of Releasing a Localized Water Molecule from the Binding Pockets of Proteins:? Double-Decoupling Method. J. Am. Chem. Soc. 2004, 126, 7683–7689.(53) Michel, J.; Tirado-Rives, J.; Jorgensen W. L. Prediction of the water content in protein binding sites. J. Phys. Chem. B 2009, 113, 13337-13346. (54) Michel, J.; Tirado-Rives, J.; Jorgensen W. L. Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization. J. Am. Chem. Soc. 2009, 131, 15403-15411.(55) Vukovic, S.; Brennan, P. E.; Huggins, D. J. Exploring the Role of Water in Molecular Recognition: Predicting Protein Ligandability Using a Combinatorial Search of Surface Hydration Sites. J. Phys. Condens. Matter 2016, 28, 344007.(56) Dunitz, J. D. The Entropic Cost of Bound Water in Crystals and Biomolecules. Science 1994, 264, 670–670.(57) Henchman, R. H.; Cockram, S. J. Water’s Non-Tetrahedral Side. Faraday Discuss. 2013, 167, 529.Figure 1. Evaluation of binding energies of a region s, typically in the vicinity of residues or pockets of a protein P. Proteins are depicted by large blue spheres. In all GCT analyses, water molecules (red circles) inside the monitored regions, sP(1,2...n), contribute to the computed binding free energies, whereas those that are out of the monitored regions (in blue) are not considered. The subscript rp indicates that the protein coordinates were restrained during the analysis. Figure 2. The average values of ?Gw,P(rp)s,w (red), ?Hw,P(rp)s,w (blue), -T?Sw,P(rp)s,w (green) around all the amino acids. The error bars represent the standard error of the mean.All plots were generated with the ggplot2 package of R unless stated otherwise.48 Figure 3. A) One example of an empirical distribution of water free energies around alanine side-chains. B) Heatmap of Kolmogorov-Smirnov D statistics between empirical cumulative per-water ?Gw,P(rp)s,w distribution functions. D values range from 0 (white) to 0.7415 (red). Figure 4. The average values of ?Gw,P(rp)s,w (red), ?Hw,P(rp)s,w (blue), -T?Sw,P(rp)s,w (green) around groups of amino acids. The shaded bars correspond to the IFST results of Beuming et al.49 For the GCT results the error bars denote the standard error of the mean. Figure 5. Two-dimensional probability distribution of hydration sites. The x axis measure the minimum distance to a hydration site observed in a X-ray diffracted protein structure. The y axis measures the density of the site relative to bulk. Probabilities are coloured from low (blue) to high (red). Figure 6. Correlation between the average magnitude of the electrostatic potential and the GCT computed binding enthalpies of hydration sites per-water, ?Hw,P(rp)s,w.Figure 7. Selected hydration sites differing considerably in the magnitude of the average electrostatic potential and the enthalpy of binding. These sites were obtained from a simulation of PDB structure 1E1X (cyclin-dependent kinase 2). Panels A), B) and C) denote various cases were the magnitude of the local electrostatic potential correlates is compared with the enthalpy of hydration of the site. Grid points related to the centroid are colored from low relative water density to high relative water density using a color range from blue-white-red. For A) the range varies from 0-16.4, B) 0-8.1 and C) 0-12.5 relative water density.Figure 8. Boxplot comparison of binding sites (red) and pockets (blue) properties. The box plots show the median and the upper and lower quartile of the distributions of per-water properties. A) Free energy and enthalpy of binding. B) Entropy of binding. C) Distributions of the number of water molecules and D) the volumes of the pockets. Outliers outside 1.5 × the interquartile range are shown as dots.Figure 9. Correlation of thermodynamic components for high-density hydration sites. A) Correlation of ?Gw,P(rp)s with -T?Sw,P(rp)s. B) Correlation of ?Gw,P(rp)swith ?Hw,P(rp)s. Figure 10. Selected hydration sites with unusual entropies of binding. A) Hydration site taken from the simulation of 1OYN. ?Gw,P(rp)s is -48.5 kcal mol?1 and -T?Sw,P(rp)s is +2.2 kcal mol?1. B) Hydration site taken from the simulation of 1E66 simulation. ?Gw,P(rp)s is -7.8 kcal mol?1 and -T?Sw,P(rp)s is -0.7 kcal mol?1. Grid points are color-coded by water density from low (blue) to high (red).Figure 11. A) Probability distribution of the components of the ?Hw,P(rp)s (red), ?Hws (blue) and ?HP(rp)s (green). The water-solute term has a long tail that extends below the left hand side of the x-axis. B) Probability distribution of the components of the entropy of binding (red), -T?Sw,P(rp)s,ori (green), -T?Sw,P(rp)s,lib (orange) and -T?Sw,P(rp)s,vib (blue).-Figure 12. Correlation plots between A) -T?Sw,P(rp)s,ori and -T?Sw,P(rp)s,lib , B) -T?Sw,P(rp)s,ori and -T?Sw,P(rp)s,vib C) -T?Sw,P(rp)s,lib and -T?Sw,P(rp)s,vib and D) ?HP(rp)s and ?Hwswith all values in kcal mol?1.Table of Contents Graphic ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download