1
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Kunze T, Dreßler C, Lauer C, Paul W, Sebastiani D. Reverse Mapping of Coarse Grained Polyglutamine Conformations from PRIME20 Sampling. Chemphyschem 2024; 25:e202300521. [PMID: 38314956 DOI: 10.1002/cphc.202300521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/07/2024]
Abstract
An inverse coarse-graining protocol is presented for generating and validating atomistic structures of large (bio-) molecules from conformations obtained via a coarse-grained sampling method. Specifically, the protocol is implemented and tested based on the (coarse-grained) PRIME20 protein model (P20/SAMC), and the resulting all-atom conformations are simulated using conventional biomolecular force fields. The phase space sampling at the coarse-grained level is performed with a stochastical approximation Monte Carlo approach. The method is applied to a series of polypeptides, specifically dimers of polyglutamine with varying chain length in aqueous solution. The majority (>70 %) of the conformations obtained from the coarse-grained peptide model can successfully be mapped back to atomistic structures that remain conformationally stable during 10 ns of molecular dynamics simulations. This work can be seen as the first step towards the overarching goal of improving our understanding of protein aggregation phenomena through simulation methods.
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Affiliation(s)
- Thomas Kunze
- Faculty of Natural Sciences II, Martin-Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120, Halle, Germany
| | - Christian Dreßler
- Institut für Physik, Ilmenau University of Technology, Weimarer Straße 32, 98693, Ilmenau, Germany
| | - Christian Lauer
- Faculty of Natural Sciences II, Martin-Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120, Halle, Germany
| | - Wolfgang Paul
- Faculty of Natural Sciences II, Martin-Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120, Halle, Germany
| | - Daniel Sebastiani
- Faculty of Natural Sciences II, Martin-Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120, Halle, Germany
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2
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Deng J, Cui Q. Efficient Sampling of Cavity Hydration in Proteins with Nonequilibrium Grand Canonical Monte Carlo and Polarizable Force Fields. J Chem Theory Comput 2024; 20:1897-1911. [PMID: 38417108 DOI: 10.1021/acs.jctc.4c00013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Prediction of the hydration levels of protein cavities and active sites is important to both mechanistic analysis and ligand design. Due to the unique microscopic environment of these buried water molecules, a polarizable model is expected to be crucial for an accurate treatment of protein internal hydration in simulations. Here we adapt a nonequilibrium candidate Monte Carlo approach for conducting grand canonical Monte Carlo simulations with the Drude polarizable force field. The GPU implementation enables the efficient sampling of internal cavity hydration levels in biomolecular systems. We also develop an enhanced sampling approach referred to as B-walking, which satisfies detailed balance and readily combines with grand canonical integration to efficiently calculate quantitative binding free energies of water to protein cavities. Applications of these developments are illustrated in a solvent box and the polar ligand binding site in trypsin. Our simulation results show that including electronic polarization leads to a modest but clear improvement in the description of water position and occupancy compared to the crystal structure. The B-walking approach enhances the range of water sampling in different chemical potential windows and thus improves the accuracy of water binding free energy calculations.
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Affiliation(s)
- Jiahua Deng
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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3
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Bryan DR, Kulp JL, Mahapatra MK, Bryan RL, Viswanathan U, Carlisle MN, Kim S, Schutte WD, Clarke KV, Doan TT, Kulp JL. BMaps: A Web Application for Fragment-Based Drug Design and Compound Binding Evaluation. J Chem Inf Model 2023; 63:4229-4236. [PMID: 37406353 DOI: 10.1021/acs.jcim.3c00209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Fragment-based drug design uses data about where, and how strongly, small chemical fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have been successfully using fragment data, derived from thermodynamically rigorous Monte Carlo fragment-protein binding simulations, in dozens of preclinical drug programs. However, this approach has not been available to the broader research community because of the cost and complexity of doing simulations and using design tools. We have developed a web application, called BMaps, to make fragment-based drug design widely available with greatly simplified user interfaces. BMaps provides access to a large repository (>550) of proteins with 100s of precomputed fragment maps, druggable hot spots, and high-quality water maps. Users can also employ their own structures or those from the Protein Data Bank and AlphaFold DB. Multigigabyte data sets are searched to find fragments in bondable orientations, ranked by a binding-free energy metric. The designers use this to select modifications that improve affinity and other properties. BMaps is unique in combining conventional tools such as docking and energy minimization with fragment-based design, in a very easy to use and automated web application. The service is available at https://www.boltzmannmaps.com.
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Affiliation(s)
- Daniel R Bryan
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
| | - John L Kulp
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
- Zymergen, Inc., 430 E. 29th Street, Suite 625, New York, New York 10016, United States
| | - Manoj K Mahapatra
- Kanak Manjari Institute of Pharmaceutical Sciences, Rourkela 769015, Odisha, India
| | - Richard L Bryan
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
| | - Usha Viswanathan
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
| | - Micah N Carlisle
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
| | - Surim Kim
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
- Zymergen, Inc., 430 E. 29th Street, Suite 625, New York, New York 10016, United States
| | - William D Schutte
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
| | - Kevaughn V Clarke
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
| | - Tony T Doan
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
| | - John L Kulp
- Conifer Point Pharmaceuticals, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
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4
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Melling O, Samways ML, Ge Y, Mobley DL, Essex JW. Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo. J Chem Theory Comput 2023; 19:1050-1062. [PMID: 36692215 PMCID: PMC9933432 DOI: 10.1021/acs.jctc.2c00823] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Indexed: 01/25/2023]
Abstract
Water molecules play a key role in many biomolecular systems, particularly when bound at protein-ligand interfaces. However, molecular simulation studies on such systems are hampered by the relatively long time scales over which water exchange between a protein and solvent takes place. Grand canonical Monte Carlo (GCMC) is a simulation technique that avoids this issue by attempting the insertion and deletion of water molecules within a given structure. The approach is constrained by low acceptance probabilities for insertions in congested systems, however. To address this issue, here, we combine GCMC with nonequilibium candidate Monte Carlo (NCMC) to yield a method that we refer to as grand canonical nonequilibrium candidate Monte Carlo (GCNCMC), in which the water insertions and deletions are carried out in a gradual, nonequilibrium fashion. We validate this new approach by comparing GCNCMC and GCMC simulations of bulk water and three protein binding sites. We find that not only is the efficiency of the water sampling improved by GCNCMC but that it also results in increased sampling of ligand conformations in a protein binding site, revealing new water-mediated ligand-binding geometries that are not observed using alternative enhanced sampling techniques.
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Affiliation(s)
- Oliver
J. Melling
- School
of Chemistry, University of Southampton, SouthamptonSO17 1BJ, U.K.
| | - Marley L. Samways
- School
of Chemistry, University of Southampton, SouthamptonSO17 1BJ, U.K.
| | - Yunhui Ge
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California92697, United States
| | - David L. Mobley
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California92697, United States
- Department
of Chemistry, University of California, Irvine, California92697, United States
| | - Jonathan W. Essex
- School
of Chemistry, University of Southampton, SouthamptonSO17 1BJ, U.K.
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5
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Samways M, Bruce Macdonald HE, Taylor RD, Essex JW. Water Networks in Complexes between Proteins and FDA-Approved Drugs. J Chem Inf Model 2023; 63:387-396. [PMID: 36469670 PMCID: PMC9832485 DOI: 10.1021/acs.jcim.2c01225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.
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Affiliation(s)
- Marley
L. Samways
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.
| | - Hannah E. Bruce Macdonald
- Computational
and Systems Biology Program, Memorial Sloan
Kettering Cancer Center, New York, New York 10065, United States
| | | | - Jonathan W. Essex
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.,
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6
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Ge Y, Melling OJ, Dong W, Essex JW, Mobley DL. Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo. J Comput Aided Mol Des 2022; 36:767-779. [PMID: 36198874 PMCID: PMC9869699 DOI: 10.1007/s10822-022-00479-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/15/2022] [Indexed: 01/26/2023]
Abstract
Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.
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Affiliation(s)
- Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA
| | - Oliver J Melling
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK
| | - Weiming Dong
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA.
- Department of Chemistry, University of California,Irvine, Irvine, CA, 92697, USA.
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7
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Barhaghi MS, Crawford B, Schwing G, Hardy DJ, Stone JE, Schwiebert L, Potoff J, Tajkhorshid E. py-MCMD: Python Software for Performing Hybrid Monte Carlo/Molecular Dynamics Simulations with GOMC and NAMD. J Chem Theory Comput 2022; 18:4983-4994. [PMID: 35621307 PMCID: PMC9760104 DOI: 10.1021/acs.jctc.1c00911] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
py-MCMD, an open-source Python software, provides a robust workflow layer that manages communication of relevant system information between the simulation engines NAMD and GOMC and generates coherent thermodynamic properties and trajectories for analysis. To validate the workflow and highlight its capabilities, hybrid Monte Carlo/molecular dynamics (MC/MD) simulations are performed for SPC/E water in the isobaric-isothermal (NPT) and grand canonical (GC) ensembles as well as with Gibbs ensemble Monte Carlo (GEMC). The hybrid MC/MD approach shows close agreement with reference MC simulations and has a computational efficiency that is 2 to 136 times greater than traditional Monte Carlo simulations. MC/MD simulations performed for water in a graphene slit pore illustrate significant gains in sampling efficiency when the coupled-decoupled configurational-bias MC (CD-CBMC) algorithm is used compared with simulations using a single unbiased random trial position. Simulations using CD-CBMC reach equilibrium with 25 times fewer cycles than simulations using a single unbiased random trial position, with a small increase in computational cost. In a more challenging application, hybrid grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations are used to hydrate a buried binding pocket in bovine pancreatic trypsin inhibitor. Water occupancies produced by GCMC/MD simulations are in close agreement with crystallographically identified positions, and GCMC/MD simulations have a computational efficiency that is 5 times better than MD simulations. py-MCMD is available on GitHub at https://github.com/GOMC-WSU/py-MCMD.
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Affiliation(s)
- Mohammad Soroush Barhaghi
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Brad Crawford
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Gregory Schwing
- Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States
| | - David J Hardy
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - John E Stone
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Loren Schwiebert
- Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Jeffrey Potoff
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Emad Tajkhorshid
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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8
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Jayaprakash P, Biswal J, Rangaswamy R, Jeyakanthan J. Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches. Mol Divers 2022:10.1007/s11030-022-10470-0. [PMID: 35727438 DOI: 10.1007/s11030-022-10470-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/27/2022] [Indexed: 10/18/2022]
Abstract
Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.
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Affiliation(s)
- Prajisha Jayaprakash
- Structural Biology and Bio-Computing Laboratory, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Jayashree Biswal
- Structural Biology and Bio-Computing Laboratory, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Raghu Rangaswamy
- Structural Biology and Bio-Computing Laboratory, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Jeyaraman Jeyakanthan
- Structural Biology and Bio-Computing Laboratory, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
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9
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Ge Y, Wych DC, Samways ML, Wall ME, Essex JW, Mobley DL. Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques. J Chem Theory Comput 2022; 18:1359-1381. [PMID: 35148093 PMCID: PMC9241631 DOI: 10.1021/acs.jctc.1c00590] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation time scales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed the rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.
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Affiliation(s)
- Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
| | - David C Wych
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Marley L Samways
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Michael E Wall
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
- Department of Chemistry, University of California, Irvine, California 92697, United States
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10
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Samways ML, Taylor RD, Bruce Macdonald HE, Essex JW. Water molecules at protein-drug interfaces: computational prediction and analysis methods. Chem Soc Rev 2021; 50:9104-9120. [PMID: 34184009 DOI: 10.1039/d0cs00151a] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.
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Affiliation(s)
- Marley L Samways
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
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11
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Bodnarchuk MS, Cassar DJ, Kettle JG, Robb G, Ward RA. Drugging the undruggable: a computational chemist's view of KRAS G12C. RSC Med Chem 2021; 12:609-614. [PMID: 34046632 DOI: 10.1039/d1md00055a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 03/18/2021] [Indexed: 01/18/2023] Open
Abstract
In recent years, the emergence of targeted covalent inhibitors which bind to the G12C mutant of KRAS have offered a solution to this previously intractable target. Inhibitors of KRASG12C tend to be structurally complex, displaying features such as atropisomerism, chiral centres and a reactive covalent warhead. Such molecules result in lengthy and challenging syntheses, and as a consequence critical decisions need to be made at the design level to maximise the chances of success. Here we take a retrospective look into how computational chemistry can help guide and prioritise medicinal chemistry efforts in the context of a series of conformationally restricted tetracyclic quinolines.
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Affiliation(s)
| | | | | | - Graeme Robb
- Oncology R&D, AstraZeneca Cambridge CB4 0WG UK
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12
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Lee TS, Allen BK, Giese TJ, Guo Z, Li P, Lin C, McGee TD, Pearlman DA, Radak BK, Tao Y, Tsai HC, Xu H, Sherman W, York DM. Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery. J Chem Inf Model 2020; 60:5595-5623. [PMID: 32936637 PMCID: PMC7686026 DOI: 10.1021/acs.jcim.0c00613] [Citation(s) in RCA: 163] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.
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Affiliation(s)
- Tai-Sung Lee
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Bryce K. Allen
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Timothy J. Giese
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Zhenyu Guo
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Pengfei Li
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Charles Lin
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - T. Dwight McGee
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - David A. Pearlman
- QSimulate Incorporated, Cambridge, Massachusetts 02139, United States
| | - Brian K. Radak
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Yujun Tao
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Hsu-Chun Tsai
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
| | - Huafeng Xu
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Silicon Therapeutics, Boston, Massachusetts 02210, United States
| | - Darrin M. York
- Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, United States
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Samways ML, Bruce Macdonald HE, Essex JW. grand: A Python Module for Grand Canonical Water Sampling in OpenMM. J Chem Inf Model 2020; 60:4436-4441. [DOI: 10.1021/acs.jcim.0c00648] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Marley L. Samways
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Jonathan W. Essex
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, United Kingdom
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