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Eberhardt J, Forli S. WaterKit: Thermodynamic Profiling of Protein Hydration Sites. J Chem Theory Comput 2023; 19:2535-2556. [PMID: 37094087 PMCID: PMC10732097 DOI: 10.1021/acs.jctc.2c01087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.
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Affiliation(s)
- Jerome Eberhardt
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
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2
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Park S, Seok C. GalaxyWater-CNN: Prediction of Water Positions on the Protein Structure by a 3D-Convolutional Neural Network. J Chem Inf Model 2022; 62:3157-3168. [PMID: 35749367 DOI: 10.1021/acs.jcim.2c00306] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Proteins interact with numerous water molecules to perform their physiological functions in biological organisms. Most water molecules act as solvent media; hence, their roles may be considered implicitly in theoretical treatments of protein structure and function. However, some water molecules interact intimately with proteins and require explicit treatment to understand their effects. Most physics-based computational methods are limited in their ability to accurately locate water molecules on protein surfaces because of inaccurate energy functions. Instead of relying on an energy function, this study attempts to learn the locations of water molecules from structural data. GalaxyWater-convolutional neural network (CNN) predicts water positions on protein chains, protein-protein interfaces, and protein-compound binding sites using a 3D-CNN model that is trained to generate a water score map on a given protein structure. The training data are compiled from high-resolution protein crystal structures resolved together with water molecules. GalaxyWater-CNN shows improved water prediction performance both in the coverage of crystal water molecules and in the accuracy of the predicted water positions when compared with previous energy-based methods. This method shows a superior performance in predicting water molecules that form hydrogen-bond networks precisely. The web service and the source code of this water prediction method are freely available at https://galaxy.seoklab.org/gwcnn and https://github.com/seoklab/GalaxyWater-CNN, respectively.
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Affiliation(s)
- Sangwoo Park
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.,Galux Inc., Gwanak-gu, Seoul 08738, Republic of Korea
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Site Density Functional Theory and Structural Bioinformatics Analysis of the SARS-CoV Spike Protein and hACE2 Complex. Molecules 2022; 27:molecules27030799. [PMID: 35164065 PMCID: PMC8839245 DOI: 10.3390/molecules27030799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/20/2022] [Accepted: 01/21/2022] [Indexed: 12/02/2022] Open
Abstract
The entry of the SARS-CoV-2, a causative agent of COVID-19, into human host cells is mediated by the SARS-CoV-2 spike (S) glycoprotein, which critically depends on the formation of complexes involving the spike protein receptor-binding domain (RBD) and the human cellular membrane receptor angiotensin-converting enzyme 2 (hACE2). Using classical site density functional theory (SDFT) and structural bioinformatics methods, we investigate binding and conformational properties of these complexes and study the overlooked role of water-mediated interactions. Analysis of the three-dimensional reference interaction site model (3DRISM) of SDFT indicates that water mediated interactions in the form of additional water bridges strongly increases the binding between SARS-CoV-2 spike protein and hACE2 compared to SARS-CoV-1-hACE2 complex. By analyzing structures of SARS-CoV-2 and SARS-CoV-1, we find that the homotrimer SARS-CoV-2 S receptor-binding domain (RBD) has expanded in size, indicating large conformational change relative to SARS-CoV-1 S protein. Protomer with the up-conformational form of RBD, which binds with hACE2, exhibits stronger intermolecular interactions at the RBD-ACE2 interface, with differential distributions and the inclusion of specific H-bonds in the CoV-2 complex. Further interface analysis has shown that interfacial water promotes and stabilizes the formation of CoV-2/hACE2 complex. This interaction causes a significant structural rigidification of the spike protein, favoring proteolytic processing of the S protein for the fusion of the viral and cellular membrane. Moreover, conformational dynamics simulations of RBD motions in SARS-CoV-2 and SARS-CoV-1 point to the role in modification of the RBD dynamics and their impact on infectivity.
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Päslack C, Das CK, Schlitter J, Schäfer LV. Spectrally Resolved Estimation of Water Entropy in the Active Site of Human Carbonic Anhydrase II. J Chem Theory Comput 2021; 17:5409-5418. [PMID: 34259506 DOI: 10.1021/acs.jctc.1c00554] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A major challenge in understanding ligand binding to biomacromolecules lies in dissecting the underlying thermodynamic driving forces at the atomic level. Quantifying the contributions of water molecules is often especially demanding, although they can play important roles in biomolecular recognition and binding processes. One example is human carbonic anhydrase II, whose active site harbors a conserved network of structural water molecules that are essential for enzymatic catalysis. Inhibitor binding disrupts this water network and changes the hydrogen-bonding patterns in the active site. Here, we use atomistic molecular dynamics simulations to compute the absolute entropy of the individual water molecules confined in the active site of hCAII using a spectrally resolved estimation (SRE) approach. The entropy decrease of water molecules that remain in the active site upon binding of a dorzolamide inhibitor is caused by changes in hydrogen bonding and stiffening of the hydrogen-bonding network. Overall, this entropy decrease is overcompensated by the gain due to the release of three water molecules from the active site upon inhibitor binding. The spectral density calculations enable the assignment of the changes to certain vibrational modes. In addition, the range of applicability of the SRE approximation is systematically explored by exploiting the gradually changing degree of immobilization of water molecules as a function of the distance to a phospholipid bilayer surface, which defines an "entropy ruler". These results demonstrate the applicability of SRE to biomolecular solvation, and we expect it to become a useful method for entropy calculations in biomolecular systems.
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Affiliation(s)
| | - Chandan K Das
- Theoretical Chemistry, Ruhr University Bochum, D-44780 Bochum, Germany
| | | | - Lars V Schäfer
- Theoretical Chemistry, Ruhr University Bochum, D-44780 Bochum, Germany
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5
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Heo L, Park S, Seok C. GalaxyWater-wKGB: Prediction of Water Positions on Protein Structure Using wKGB Statistical Potential. J Chem Inf Model 2021; 61:2283-2293. [PMID: 33938216 DOI: 10.1021/acs.jcim.0c01434] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Proteins fold and function in water, and protein-water interactions play important roles in protein structure and function. In computational studies on protein structure and interaction, the effect of water is considered either implicitly or explicitly. Implicit water models are frequently used in protein structure prediction and docking because they are computationally much more efficient than explicit water models, which are often employed in molecular dynamics (MD) simulations. However, implicit water models that treat water as a continuous solvent medium cannot account for specific atomistic protein-water interactions that are critical for structure formation and interactions with other molecules. Various methods for predicting water molecules that form specific atomistic interactions with proteins have been developed. Methods involving MD simulations or the integral equation theory tend to produce more accurate results at a higher computational cost than simple geometry- or energy-based methods. Here, we present a novel method for predicting water positions on a protein surface called GalaxyWater-wKGB, which is based on a statistical potential, a water knowledge-based potential based on the generalized Born model (wKGB). This method is accurate and rapid because it does not require conformational sampling or iterative computation owing to the effective statistical treatment employed to derive the potential. The statistical potential describes specific protein atom-water interactions more accurately than conventional potentials by considering the dependence on the degree of solvent accessibility of protein atoms as well as on protein atom-water distances and orientations. The introduction of solvent accessibility allows effective consideration of competing nonspecific protein-water and intraprotein interactions. When tested on high-resolution protein crystal structures, this method could recover similar or larger fractions of crystallographic water 180 times faster than the sophisticated integral equation theory, 3D-RISM. A web service of this water prediction method is freely available at http://galaxy.seoklab.org/wkgb.
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Affiliation(s)
- Lim Heo
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Sangwoo Park
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
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Chuev GN, Fedotova MV, Valiev M. Chemical bond effects in classical site density functional theory of inhomogeneous molecular liquids. J Chem Phys 2020; 152:041101. [PMID: 32007044 DOI: 10.1063/1.5139619] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Intra-molecular interactions or chemical bonds represent one of the main distinguishing characteristics of molecular fluids. Development of accurate and practical methods to treat these effects is one of the long standing problems in classical site density functional theory (SDFT). One particular instance when these issues become particularly severe is the case of classical interaction potentials with auxiliary sites or dummy atoms. In this situation, current SDFT implementations, such as the three-dimensional reference interaction site model, lead to nonphysical results. We re-examine this issue in this work using our recent reformulation of SDFT (Valiev and Chuev, J. Stat. Mech.: Theory Exp. 2018, 093201). We put forward a simple practical solution to this problem and illustrate its utility for the case of spherical solutes in diatomic liquids.
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Affiliation(s)
- Gennady N Chuev
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Science, Pushchino, Moscow Region 142290, Russia
| | - Marina V Fedotova
- G.A. Krestov Institute of Solution Chemistry, Russian Academy of Sciences, Akademicheskaya St., 1, 153045 Ivanovo, Russia
| | - Marat Valiev
- Molecular Sciences Software Group, Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, USA
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Cappel D, Jerome S, Hessler G, Matter H. Impact of Different Automated Binding Pose Generation Approaches on Relative Binding Free Energy Simulations. J Chem Inf Model 2020; 60:1432-1444. [DOI: 10.1021/acs.jcim.9b01118] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
| | - Steven Jerome
- Schrödinger Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Gerhard Hessler
- Integrated Drug Discovery (IDD), Synthetic Molecular Design, Sanofi-Aventis Deutschland GmbH, Building G838, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Hans Matter
- Integrated Drug Discovery (IDD), Synthetic Molecular Design, Sanofi-Aventis Deutschland GmbH, Building G838, Industriepark Höchst, 65926 Frankfurt am Main, Germany
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Continuous molecular fields and the concept of molecular co-fields in structure-activity studies. Future Med Chem 2019; 11:2701-2713. [PMID: 31596146 DOI: 10.4155/fmc-2018-0360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The analysis of information on the spatial structure of molecules and the physical fields of their interactions with biological targets is extremely important for solving various problems in drug discovery. This mini-review article surveys the main features of the continuous molecular fields approach and its use for analyzing structure-activity relationships in 3D space, building 3D quantitative structure-activity models and conducting similarity based virtual screening. Particular attention is paid to the consideration of the concept of molecular co-fields and their use for the interpretation of 3D structure-activity models. The principles of molecular design based on the overlapping and the similarity of molecular fields with corresponding co-fields are formulated.
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Nguyen C, Yamazaki T, Kovalenko A, Case DA, Gilson MK, Kurtzman T, Luchko T. A molecular reconstruction approach to site-based 3D-RISM and comparison to GIST hydration thermodynamic maps in an enzyme active site. PLoS One 2019; 14:e0219473. [PMID: 31291328 PMCID: PMC6619770 DOI: 10.1371/journal.pone.0219473] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 06/24/2019] [Indexed: 11/25/2022] Open
Abstract
Computed, high-resolution, spatial distributions of solvation energy and entropy can provide detailed information about the role of water in molecular recognition. While grid inhomogeneous solvation theory (GIST) provides rigorous, detailed thermodynamic information from explicit solvent molecular dynamics simulations, recent developments in the 3D reference interaction site model (3D-RISM) theory allow many of the same quantities to be calculated in a fraction of the time. However, 3D-RISM produces atomic-site, rather than molecular, density distributions, which are difficult to extract physical meaning from. To overcome this difficulty, we introduce a method to reconstruct molecular density distributions from atomic-site density distributions. Furthermore, we assess the quality of the resulting solvation thermodynamics density distributions by analyzing the binding site of coagulation Factor Xa with both GIST and 3D-RISM. We find good qualitative agreement between the methods for oxygen and hydrogen densities as well as direct solute-solvent energetic interactions. However, 3D-RISM predicts lower energetic and entropic penalties for moving water from the bulk to the binding site.
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Affiliation(s)
- Crystal Nguyen
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | | | - Andriy Kovalenko
- National Institute for Nanotechnology, National Research Council of Canada, Edmonton, Alberta, Canada
- Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - David A. Case
- Department of Chemistry, Lehman College, The City University of New York, Bronx, New York, United States of America
| | - Michael K. Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Tom Kurtzman
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, United States of America
| | - Tyler Luchko
- Department of Physics and Astronomy, California State University, Northridge, California, United States of America
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10
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Cao S, Konovalov KA, Unarta IC, Huang X. Recent Developments in Integral Equation Theory for Solvation to Treat Density Inhomogeneity at Solute–Solvent Interface. ADVANCED THEORY AND SIMULATIONS 2019. [DOI: 10.1002/adts.201900049] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Siqin Cao
- Department of Chemistrythe Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong
- Center of System Biology and Human HealthState Key Laboratory of Molecular Neuroscience, Hong Kong Branch Clear Water Bay Kowloon Hong Kong
| | - Kirill A. Konovalov
- Department of Chemistrythe Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong
- Center of System Biology and Human HealthState Key Laboratory of Molecular Neuroscience, Hong Kong Branch Clear Water Bay Kowloon Hong Kong
| | - Ilona Christy Unarta
- Center of System Biology and Human HealthState Key Laboratory of Molecular Neuroscience, Hong Kong Branch Clear Water Bay Kowloon Hong Kong
- Bioengineering Graduate Programthe Hong Kong University of Science and TechnologyHong Kong of Chinese National EngineeringResearch Center for Tissue Restoration and Reconstructionthe Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong
| | - Xuhui Huang
- Department of Chemistrythe Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong
- Center of System Biology and Human HealthState Key Laboratory of Molecular Neuroscience, Hong Kong Branch Clear Water Bay Kowloon Hong Kong
- Bioengineering Graduate Programthe Hong Kong University of Science and TechnologyHong Kong of Chinese National EngineeringResearch Center for Tissue Restoration and Reconstructionthe Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong
- HKUST‐Shenzhen Research Institute Hi‐Tech Park, Nanshan Shenzhen 518057 China
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11
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Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies. Future Med Chem 2019; 11:1177-1193. [PMID: 30799643 DOI: 10.4155/fmc-2018-0435] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies.
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Sosnin S, Misin M, Palmer DS, Fedorov MV. 3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2018; 30:32LT03. [PMID: 29964270 DOI: 10.1088/1361-648x/aad076] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this work, we present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These spatial distributions are obtained by a molecular theory called three-dimensional reference interaction site model. We have shown that the method allows one to achieve a good accuracy of prediction of bioconcentration factor which is difficult to predict by direct application of methods of molecular theory or simulations. Our research demonstrates that combination of molecular theories with modern machine learning approaches can be effectively used for predicting properties that are otherwise inaccessible to purely theory-based models.
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Affiliation(s)
- Sergey Sosnin
- Center for Computational and Data-intensive Science and Engineering, Skolkovo Institute of Science and Technology, Nobelya Ulitsa 3 Moscow, 121205, Russia
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Matter H, Güssregen S. Characterizing hydration sites in protein-ligand complexes towards the design of novel ligands. Bioorg Med Chem Lett 2018; 28:2343-2352. [PMID: 29880400 DOI: 10.1016/j.bmcl.2018.05.061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 05/28/2018] [Accepted: 05/30/2018] [Indexed: 11/18/2022]
Abstract
Water is an essential part of protein binding sites and mediates interactions to ligands. Its displacement by ligand parts affects the free binding energy of resulting protein-ligand complexes. Therefore the characterization of solvation properties is important for design. Of particular interest is the propensity of localized water to be favorably displaced by a ligand. This review discusses two popular computational approaches addressing these questions, namely WaterMap based on statistical mechanics analysis of MD simulations and 3D RISM based on integral equation theory of liquids. The theoretical background and recent applications in structure-based design will be presented.
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Affiliation(s)
- Hans Matter
- Sanofi-Aventis Deutschland GmbH, Integrated Drug Discovery (IDD), Synthetic Molecular Design, Building G838, Industriepark Höchst, 65926 Frankfurt am Main, Germany.
| | - Stefan Güssregen
- Sanofi-Aventis Deutschland GmbH, Integrated Drug Discovery (IDD), Synthetic Molecular Design, Building G838, Industriepark Höchst, 65926 Frankfurt am Main, Germany
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14
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Ansari SM, Palmer DS. Comparative Molecular Field Analysis Using Molecular Integral Equation Theory. J Chem Inf Model 2018; 58:1253-1265. [DOI: 10.1021/acs.jcim.7b00600] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Samiul M. Ansari
- Department of Pure and Applied Chemistry, University of Strathclyde, Thomas Graham Building, 295 Cathedral Street, Glasgow, Scotland G1 1XL, U.K
| | - David S. Palmer
- Department of Pure and Applied Chemistry, University of Strathclyde, Thomas Graham Building, 295 Cathedral Street, Glasgow, Scotland G1 1XL, U.K
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