1
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Baumann HM, Mobley DL. Impact of protein conformations on binding free energy calculations in the beta-secretase 1 system. J Comput Chem 2024; 45:2024-2033. [PMID: 38725239 PMCID: PMC11236511 DOI: 10.1002/jcc.27365] [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] [Received: 09/12/2023] [Revised: 01/13/2024] [Accepted: 03/24/2024] [Indexed: 07/11/2024]
Abstract
In binding free energy calculations, simulations must sample all relevant conformations of the system in order to obtain unbiased results. For instance, different ligands can bind to different metastable states of a protein, and if these protein conformational changes are not sampled in relative binding free energy calculations, the contribution of these states to binding is not accounted for and thus calculated binding free energies are inaccurate. In this work, we investigate the impact of different beta-sectretase 1 (BACE1) protein conformations obtained from x-ray crystallography on the binding of BACE1 inhibitors. We highlight how these conformational changes are not adequately sampled in typical molecular dynamics simulations. Furthermore, we show that insufficient sampling of relevant conformations induces substantial error in relative binding free energy calculations, as judged by a variation in calculated relative binding free energies up to 2 kcal/mol depending on the starting protein conformation. These results emphasize the importance of protein conformational sampling and pose this BACE1 system as a challenge case for further method development in the area of enhanced protein conformational sampling, either in combination with binding calculations or as an endpoint correction.
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Affiliation(s)
- Hannah M Baumann
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California, USA
| | - David L Mobley
- Department of Chemistry, University of California, Irvine, California, USA
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2
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Zhao M, Yu W, MacKerell AD. Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms. J Phys Chem B 2024; 128:7362-7375. [PMID: 39031121 PMCID: PMC11294009 DOI: 10.1021/acs.jpcb.4c03045] [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: 07/22/2024]
Abstract
In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.
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Affiliation(s)
- Mingtian Zhao
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
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3
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Hayes RL, Cervantes LF, Abad Santos JC, Samadi A, Vilseck JZ, Brooks CL. How to Sample Dozens of Substitutions per Site with λ Dynamics. J Chem Theory Comput 2024; 20:6098-6110. [PMID: 38976796 PMCID: PMC11270746 DOI: 10.1021/acs.jctc.4c00514] [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] [Received: 04/17/2024] [Revised: 06/18/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024]
Abstract
Alchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.
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Affiliation(s)
- Ryan L. Hayes
- Department
of Chemical and Biomolecular Engineering, University of California Irvine, Irvine, California 92697, United States
- Department
of Pharmaceutical Sciences, University of
California Irvine, Irvine, California 92697, United States
| | - Luis F. Cervantes
- Department
of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Justin Cruz Abad Santos
- Department
of Chemical and Biomolecular Engineering, University of California Irvine, Irvine, California 92697, United States
| | - Amirmasoud Samadi
- Department
of Chemical and Biomolecular Engineering, University of California Irvine, Irvine, California 92697, United States
| | - Jonah Z. Vilseck
- Department
of Biochemistry and Molecular Biology, Indiana
University School of Medicine, Indianapolis, Indiana 46202, United States
- Center
for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Charles L. Brooks
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Biophysics
Program, University of Michigan, Ann Arbor, Michigan 48109, United States
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4
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Gilson MK, Stewart LE, Potter MJ, Webb SP. Rapid, Accurate, Ranking of Protein-Ligand Binding Affinities with VM2, the Second-Generation Mining Minima Method. J Chem Theory Comput 2024; 20:6328-6340. [PMID: 38989926 DOI: 10.1021/acs.jctc.4c00407] [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: 07/12/2024]
Abstract
The structure-based technologies most widely used to rank the affinities of candidate small molecule drugs for proteins range from faster but less reliable docking methods to slower but more accurate explicit solvent free energy methods. In recent years, we have advanced another technology, which is called mining minima because it "mines" out the main contributions to the chemical potentials of the free and bound molecular species by identifying and characterizing their main local energy minima. The present study provides systematic benchmarks of the accuracy and computational speed of mining minima, as implemented in the VeraChem Mining Minima Generation 2 (VM2) code, across two well-regarded protein-ligand benchmark data sets, for which there are already benchmark data for docking, free energy, and other computational methods. A core result is that VM2's accuracy approaches that of explicit solvent free energy methods at a far lower computational cost. In finer-grained analyses, we also examine the influence of various run settings, such as the treatment of crystallographic water molecules, on the accuracy, and define the costs in time and dollars of representative runs on Amazon Web Services (AWS) compute instances with various CPU and GPU combinations. We also use the benchmark data to determine the importance of VM2's correction from generalized Born to finite-difference Poisson-Boltzmann results for each energy well and find that this correction affords a remarkably consistent improvement in accuracy at a modest computational cost. The present results establish VM2 as a distinctive technology for early-stage drug discovery, which provides a strong combination of efficiency and predictivity.
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Affiliation(s)
- Michael K Gilson
- VeraChem LLC, 12850 Middlebrook Rd, Ste 205, Germantown, Maryland 20874, United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States
| | - Lawrence E Stewart
- VeraChem LLC, 12850 Middlebrook Rd, Ste 205, Germantown, Maryland 20874, United States
| | - Michael J Potter
- VeraChem LLC, 12850 Middlebrook Rd, Ste 205, Germantown, Maryland 20874, United States
| | - Simon P Webb
- VeraChem LLC, 12850 Middlebrook Rd, Ste 205, Germantown, Maryland 20874, United States
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5
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Aho N, Groenhof G, Buslaev P. Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path? J Chem Theory Comput 2024. [PMID: 39039621 DOI: 10.1021/acs.jctc.4c00134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.
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Affiliation(s)
- Noora Aho
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014 Jyväskylä, Finland
- Theoretical Physics and Center for Biophysics, Saarland University, 66123 Saarbrücken, Germany
| | - Gerrit Groenhof
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Pavel Buslaev
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014 Jyväskylä, Finland
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6
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Zhang H, Im W. Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods. J Chem Inf Model 2024; 64:5671-5679. [PMID: 38959405 PMCID: PMC11267607 DOI: 10.1021/acs.jcim.4c00764] [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] [Received: 05/02/2024] [Revised: 06/12/2024] [Accepted: 06/25/2024] [Indexed: 07/05/2024]
Abstract
Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.
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Affiliation(s)
- Han Zhang
- Departments of Biological
Sciences and Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological
Sciences and Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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7
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Pala D, Clark DE. Caught between a ROCK and a hard place: current challenges in structure-based drug design. Drug Discov Today 2024; 29:104106. [PMID: 39029868 DOI: 10.1016/j.drudis.2024.104106] [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: 04/11/2024] [Revised: 06/27/2024] [Accepted: 07/13/2024] [Indexed: 07/21/2024]
Abstract
The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.
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Affiliation(s)
- Daniele Pala
- Medicinal Chemistry and Drug Design Technologies Department, Chiesi Farmaceutici S.p.A, Research Center, Largo Belloli 11/a, 43122 Parma, Italy
| | - David E Clark
- Charles River, 6-9 Spire Green Centre, Flex Meadow, Harlow CM19 5TR, UK.
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8
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Pereira GP, Alessandri R, Domínguez M, Araya-Osorio R, Grünewald L, Borges-Araújo L, Wu S, Marrink SJ, Souza PCT, Mera-Adasme R. Bartender: Martini 3 Bonded Terms via Quantum Mechanics-Based Molecular Dynamics. J Chem Theory Comput 2024; 20:5763-5773. [PMID: 38924075 DOI: 10.1021/acs.jctc.4c00275] [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: 06/28/2024]
Abstract
Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.
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Affiliation(s)
- Gilberto P Pereira
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, Lyon 69364, France
- Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, Lyon 69364, France
| | - Riccardo Alessandri
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Moisés Domínguez
- Departamento de Ciencias del Ambiente, Facultad de Química y Biología, Universidad de Santiago de Chile (USACH), Av. Libertador Bernardo O'Higgins 3363, Estacion Central, Santiago 9170022, Chile
| | - Rocío Araya-Osorio
- Departamento de Quimica, Facultad de Ciencias, Universidad de Tarapacá, Av. Gral. Velasquez 1775, Arica 1000000, Chile
| | - Linus Grünewald
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, Groningen 9747 AG, The Netherlands
| | - Luís Borges-Araújo
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, Lyon 69364, France
- Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, Lyon 69364, France
| | - Sangwook Wu
- PharmCADD, Busan 48792, Republic of Korea
- Department of Physics, Pukyong National University, Busan 48513, Republic of Korea
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, Groningen 9747 AG, The Netherlands
| | - Paulo C T Souza
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, Lyon 69364, France
- Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, Lyon 69364, France
| | - Raul Mera-Adasme
- Departamento de Quimica, Facultad de Ciencias, Universidad de Tarapacá, Av. Gral. Velasquez 1775, Arica 1000000, Chile
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9
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Hahn DF, Gapsys V, de Groot BL, Mobley DL, Tresadern G. Current State of Open Source Force Fields in Protein-Ligand Binding Affinity Predictions. J Chem Inf Model 2024; 64:5063-5076. [PMID: 38895959 PMCID: PMC11234369 DOI: 10.1021/acs.jcim.4c00417] [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] [Received: 03/10/2024] [Revised: 04/23/2024] [Accepted: 04/25/2024] [Indexed: 06/21/2024]
Abstract
In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.
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Affiliation(s)
- David F. Hahn
- Computational
Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse 2340, Belgium
| | - Vytautas Gapsys
- Computational
Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse 2340, Belgium
- Computational
Biomolecular Dynamics Group, Max Planck
Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen 37077, Germany
| | - Bert L. de Groot
- Computational
Biomolecular Dynamics Group, Max Planck
Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen 37077, Germany
| | - David L. Mobley
- Department
of Chemistry, University of California, Irvine, California 92697, United States
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California 92697, United States
| | - Gary Tresadern
- Computational
Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse 2340, Belgium
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10
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Schmitz B, Frieg B, Homeyer N, Jessen G, Gohlke H. Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin. Arch Pharm (Weinheim) 2024; 357:e2300612. [PMID: 38319801 DOI: 10.1002/ardp.202300612] [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] [Received: 10/20/2023] [Revised: 12/18/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024]
Abstract
Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.
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Affiliation(s)
- Birte Schmitz
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Benedikt Frieg
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Nadine Homeyer
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gisela Jessen
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich, Jülich, Germany
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11
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Amezcua M, Setiadi J, Mobley DL. The SAMPL9 host-guest blind challenge: an overview of binding free energy predictive accuracy. Phys Chem Chem Phys 2024; 26:9207-9225. [PMID: 38444308 PMCID: PMC10954238 DOI: 10.1039/d3cp05111k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/03/2024] [Indexed: 03/07/2024]
Abstract
We report the results of the SAMPL9 host-guest blind challenge for predicting binding free energies. The challenge focused on macrocycles from pillar[n]-arene and cyclodextrin host families, including WP6, and bCD and HbCD. A variety of methods were used by participants to submit binding free energy predictions. A machine learning approach based on molecular descriptors achieved the highest accuracy (RMSE of 2.04 kcal mol-1) among the ranked methods in the WP6 dataset. Interestingly, predictions for WP6 obtained via docking tended to outperform all methods (RMSE of 1.70 kcal mol-1), most of which are MD based and computationally more expensive. In general, methods applying force fields achieved better correlation with experiments for WP6 opposed to the machine learning and docking models. In the cyclodextrin-phenothiazine challenge, the ATM approach emerged as the top performing method with RMSE less than 1.86 kcal mol-1. Correlation metrics of ranked methods in this dataset were relatively poor compared to WP6. We also highlight several lessons learned to guide future work and help improve studies on the systems discussed. For example, WP6 may be present in other microstates other than its -12 state in the presence of certain guests. Machine learning approaches can be used to fine tune or help train force fields for certain chemistry (i.e. WP6-G4). Certain phenothiazines occupy distinct primary and secondary orientations, some of which were considered individually for accurate binding free energies. The accuracy of predictions from certain methods while starting from a single binding pose/orientation demonstrates the sensitivity of calculated binding free energies to the orientation, and in some cases the likely dominant orientation for the system. Computational and experimental results suggest that guest phenothiazine core traverses both the secondary and primary faces of the cyclodextrin hosts, a bulky cationic side chain will primarily occupy the primary face, and the phenothiazine core substituent resides at the larger secondary face.
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Affiliation(s)
- Martin Amezcua
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, USA.
| | - Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, USA.
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, USA
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12
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Dodds M, Guo J, Löhr T, Tibo A, Engkvist O, Janet JP. Sample efficient reinforcement learning with active learning for molecular design. Chem Sci 2024; 15:4146-4160. [PMID: 38487235 PMCID: PMC10935729 DOI: 10.1039/d3sc04653b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/07/2024] [Indexed: 03/17/2024] Open
Abstract
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties. In silico methods, such as virtual library screening (VS) and de novo molecular generation with RL, show great promise in accelerating this search. However, incorporation of increasingly complex computational models in these workflows requires increasing sample efficiency. Here, we introduce an active learning system linked with an RL model (RL-AL) for molecular design, which aims to improve the sample-efficiency of the optimization process. We identity and characterize unique challenges combining RL and AL, investigate the interplay between the systems, and develop a novel AL approach to solve the MPO problem. Our approach greatly expedites the search for novel solutions relative to baseline-RL for simple ligand- and structure-based oracle functions, with a 5-66-fold increase in hits generated for a fixed oracle budget and a 4-64-fold reduction in computational time to find a specific number of hits. Furthermore, compounds discovered through RL-AL display substantial enrichment of a multi-parameter scoring objective, indicating superior efficacy in curating high-scoring compounds, without a reduction in output diversity. This significant acceleration improves the feasibility of oracle functions that have largely been overlooked in RL due to high computational costs, for example free energy perturbation methods, and in principle is applicable to any RL domain.
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Affiliation(s)
- Michael Dodds
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Jeff Guo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Thomas Löhr
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Alessandro Tibo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
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13
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Ries B, Alibay I, Swenson DWH, Baumann HM, Henry MM, Eastwood JRB, Gowers RJ. Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations. J Chem Theory Comput 2024; 20:1862-1877. [PMID: 38330251 PMCID: PMC10941767 DOI: 10.1021/acs.jctc.3c01206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/10/2024]
Abstract
Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.
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Affiliation(s)
- Benjamin Ries
- Medicinal
Chemistry, Boehringer Ingelheim Pharma GmbH
& Co KG, Birkendorfer Str 65, 88397 Biberach an der Riss, Germany
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Irfan Alibay
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - David W. H. Swenson
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Hannah M. Baumann
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Michael M. Henry
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
- Computational
and Systems Biology Program, Sloan Kettering
Institute, Memorial Sloan Kettering Cancer Center, New York, 1275 New York, United States
| | - James R. B. Eastwood
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Richard J. Gowers
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
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14
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Khuttan S, Gallicchio E. What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling. J Chem Theory Comput 2024; 20:1489-1501. [PMID: 38252868 PMCID: PMC10867849 DOI: 10.1021/acs.jctc.3c01250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.
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Affiliation(s)
- Sheenam Khuttan
- Department
of Chemistry and Biochemistry, Brooklyn
College of the City University of New York, New York, New York 11210, United States
- Ph.D.
Program in Biochemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
| | - Emilio Gallicchio
- Department
of Chemistry and Biochemistry, Brooklyn
College of the City University of New York, New York, New York 11210, United States
- Ph.D.
Program in Biochemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
- Ph.D.
Program in Chemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
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15
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Hu R, Zhang J, Kang Y, Wang Z, Pan P, Deng Y, Hsieh CY, Hou T. Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization. J Chem Theory Comput 2024; 20:1465-1478. [PMID: 38300792 DOI: 10.1021/acs.jctc.3c01154] [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: 02/03/2024]
Abstract
Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.
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Affiliation(s)
- Renling Hu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Polytechnic Institute, Zhejiang University, Hangzhou 310058, Zhejiang, China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Jintu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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16
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Huang P, Åbacka H, Wilson CJ, Wind ML, Rűtzler M, Hagström-Andersson A, Gourdon P, de Groot BL, Venskutonytė R, Lindkvist-Petersson K. Molecular basis for human aquaporin inhibition. Proc Natl Acad Sci U S A 2024; 121:e2319682121. [PMID: 38319972 PMCID: PMC10873552 DOI: 10.1073/pnas.2319682121] [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] [Received: 11/22/2023] [Accepted: 01/04/2024] [Indexed: 02/08/2024] Open
Abstract
Cancer invasion and metastasis are known to be potentiated by the expression of aquaporins (AQPs). Likewise, the expression levels of AQPs have been shown to be prognostic for survival in patients and have a role in tumor growth, edema, angiogenesis, and tumor cell migration. Thus, AQPs are key players in cancer biology and potential targets for drug development. Here, we present the single-particle cryo-EM structure of human AQP7 at 3.2-Å resolution in complex with the specific inhibitor compound Z433927330. The structure in combination with MD simulations shows that the inhibitor binds to the endofacial side of AQP7. In addition, cancer cells treated with Z433927330 show reduced proliferation. The data presented here serve as a framework for the development of AQP inhibitors.
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Affiliation(s)
- Peng Huang
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
| | - Hannah Åbacka
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
| | - Carter J. Wilson
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, 37077Gottingen, Germany
| | - Malene Lykke Wind
- Department of Biomedical Sciences, Copenhagen University, DK-2200Copenhagen N, Denmark
| | - Michael Rűtzler
- ApoGlyx, Lund22381, Sweden
- Division of Biochemistry and Structural Biology, Department of Chemistry, Lund University, Lund22100, Sweden
| | - Anna Hagström-Andersson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund22184, Sweden
| | - Pontus Gourdon
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Department of Biomedical Sciences, Copenhagen University, DK-2200Copenhagen N, Denmark
| | - Bert L. de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, 37077Gottingen, Germany
| | - Raminta Venskutonytė
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Lund Institute of Advanced Neutron and X-Ray Science, Lund22370, Sweden
| | - Karin Lindkvist-Petersson
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Lund Institute of Advanced Neutron and X-Ray Science, Lund22370, Sweden
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17
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Brooks CL, MacKerell AD, Post CB, Nilsson L. Biomolecular dynamics in the 21st century. Biochim Biophys Acta Gen Subj 2024; 1868:130534. [PMID: 38065235 PMCID: PMC10842176 DOI: 10.1016/j.bbagen.2023.130534] [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] [Received: 09/26/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024]
Abstract
The relevance of motions in biological macromolecules has been clear since the early structural analyses of proteins by X-ray crystallography. Computer simulations have been applied to provide a deeper understanding of the dynamics of biological macromolecules since 1976, and are now a standard tool in many labs working on the structure and function of biomolecules. In this mini-review we highlight some areas of current interest and active development for simulations, in particular all-atom molecular dynamics simulations.
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Affiliation(s)
- Charles L Brooks
- University of Michigan, Department of Chemistry, Ann Arbor, MI 48109, USA.
| | | | - Carol B Post
- Purdue University, Department of Medicinal Chemistry and Molecular Pharmacology, West Lafayette, IN 47907-2091, USA.
| | - Lennart Nilsson
- Karolinska Institutet, Department of Biosciences and Nutrition, SE-14183 Huddinge, Sweden.
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18
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Gelžinytė E, Öeren M, Segall MD, Csányi G. Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. J Chem Theory Comput 2024; 20:164-177. [PMID: 38108269 PMCID: PMC10782450 DOI: 10.1021/acs.jctc.3c00710] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023]
Abstract
We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs.
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Affiliation(s)
- Elena Gelžinytė
- Engineering
Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.
| | - Mario Öeren
- Optibrium
Limited, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9GL, U.K.
| | - Matthew D. Segall
- Optibrium
Limited, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9GL, U.K.
| | - Gábor Csányi
- Engineering
Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.
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19
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Chen L, Wu Y, Wu C, Silveira A, Sherman W, Xu H, Gallicchio E. Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands. J Chem Inf Model 2024; 64:250-264. [PMID: 38147877 DOI: 10.1021/acs.jcim.3c01705] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.
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Affiliation(s)
- Lieyang Chen
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Yujie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Chuanjie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Ana Silveira
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Huafeng Xu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, New York, New York 11210, United States
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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20
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Herz AM, Kellici T, Morao I, Michel J. Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities. Methods Mol Biol 2024; 2716:241-264. [PMID: 37702943 DOI: 10.1007/978-1-0716-3449-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.
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Affiliation(s)
- Anna M Herz
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK
| | - Tahsin Kellici
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
- Merck & Co., Inc., Modelling and Informatics, West Point, PA, USA
| | - Inaki Morao
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
| | - Julien Michel
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK.
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21
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Robo MT, Hayes RL, Ding X, Pulawski B, Vilseck JZ. Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling. Nat Commun 2023; 14:8515. [PMID: 38129400 PMCID: PMC10740020 DOI: 10.1038/s41467-023-44208-9] [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] [Received: 04/12/2022] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Relative binding free energy calculations have become an integral computational tool for lead optimization in structure-based drug design. Classical alchemical methods, including free energy perturbation or thermodynamic integration, compute relative free energy differences by transforming one molecule into another. However, these methods have high operational costs due to the need to perform many pairwise perturbations independently. To reduce costs and accelerate molecular design workflows, we present a method called λ-dynamics with bias-updated Gibbs sampling. This method uses dynamic biases to continuously sample between multiple ligand analogues collectively within a single simulation. We show that many relative binding free energies can be determined quickly with this approach without compromising accuracy. For five benchmark systems, agreement to experiment is high, with root mean square errors near or below 1.0 kcal mol-1. Free energy results are consistent with other computational approaches and within statistical noise of both methods (0.4 kcal mol-1 or less). Notably, large efficiency gains over thermodynamic integration of 18-66-fold for small perturbations and 100-200-fold for whole aromatic ring substitutions are observed. The rapid determination of relative binding free energies will enable larger chemical spaces to be more readily explored and structure-based drug design to be accelerated.
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Affiliation(s)
- Michael T Robo
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Biosciences Research Institute, 1210 Waterway Blvd Ste. 2000, Indianapolis, IN, 46202, USA
| | - Ryan L Hayes
- Chemical and Biomolecular Engineering, University of California, Irvine, California, 92617, USA
- Pharmaceutical Sciences, University of California, Irvine, CA, 92617, USA
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Chemistry, Tufts University, Medford, MA, 02144, USA
| | - Brian Pulawski
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Jonah Z Vilseck
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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22
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Procacci P. Dealing with Induced Fit, Conformational Selection, and Secondary Poses in Molecular Dynamics Simulations for Reliable Free Energy Predictions. J Chem Theory Comput 2023; 19:8942-8954. [PMID: 38037326 PMCID: PMC10720345 DOI: 10.1021/acs.jctc.3c00867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023]
Abstract
In this study, we have tested the performance of standard molecular dynamics (MD) simulations, replicates of shorter standard MD simulations, and Hamiltonian Replica Exchange (HREM) simulations for the sampling of two macrocyclic hosts for guest delivery, characterized by induced fit (phenyl-based host) and conformation selection (naphthyl-based host) and of the ODR-BRD4(I) drug-receptor system where the ligand can assume two main poses. For the optimization of the HREM simulation, we have proposed and tested an on-the-fly iterative scheme for equalizing the acceptance ratio along the replica progression at a constant replica number resulting in a moderate impact of the sampling efficiency. Concerning standard MD, we have found that, while splitting the total allocated simulation time in short MD replicates can reproduce the sampling efficiency of HREM in the phenyl-based host and in the ODR-BRD4(I) complex, in the naphthyl-based macrocycle, characterized by long-lived metastable states, enhanced sampling techniques are the only viable alternative for a reliable canonical sampling of the rugged conformational landscape.
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Affiliation(s)
- Piero Procacci
- Dipartimento di Chimica “Ugo
Schiff”, Università degli
Studi di Firenze, Via
della Lastruccia 3, 50019 Sesto Fiorentino, Italy
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23
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Champion C, Gall R, Ries B, Rieder SR, Barros EP, Riniker S. Accelerating Alchemical Free Energy Prediction Using a Multistate Method: Application to Multiple Kinases. J Chem Inf Model 2023; 63:7133-7147. [PMID: 37948537 PMCID: PMC10685456 DOI: 10.1021/acs.jcim.3c01469] [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] [Received: 09/13/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
Alchemical free-energy methods based on molecular dynamics (MD) simulations have become important tools to identify modifications of small organic molecules that improve their protein binding affinity during lead optimization. The routine application of pairwise free-energy methods to rank potential binders from best to worst is impacted by the combinatorial increase in calculations to perform when the number of molecules to assess grows. To address this fundamental limitation, our group has developed replica-exchange enveloping distribution sampling (RE-EDS), a pathway-independent multistate method, enabling the calculation of alchemical free-energy differences between multiple ligands (N > 2) from a single MD simulation. In this work, we apply the method to a set of four kinases with diverse binding pockets and their corresponding inhibitors (42 in total), chosen to showcase the general applicability of RE-EDS in prospective drug design campaigns. We show that for the targets studied, RE-EDS is able to model up to 13 ligands simultaneously with high sampling efficiency, leading to a substantial decrease in computational cost when compared to pairwise methods.
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Affiliation(s)
- Candide Champion
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - René Gall
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | | | - Salomé R. Rieder
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Emilia P. Barros
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Sereina Riniker
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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24
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Cano-González L, Espinosa-Mendoza JD, Matadamas-Martínez F, Romero-Velásquez A, Flores-Ramos M, Colorado-Pablo LF, Cerbón-Cervantes MA, Castillo R, González-Sánchez I, Yépez-Mulia L, Hernández-Campos A, Aguayo-Ortiz R. Structure-Based Optimization of Carbendazim-Derived Tubulin Polymerization Inhibitors through Alchemical Free Energy Calculations. J Chem Inf Model 2023; 63:7228-7238. [PMID: 37947759 DOI: 10.1021/acs.jcim.3c01379] [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: 11/12/2023]
Abstract
Carbendazim derivatives, commonly used as antiparasitic drugs, have shown potential as anticancer agents due to their ability to induce cell cycle arrest and apoptosis in human cancer cells by inhibiting tubulin polymerization. Crystallographic structures of α/β-tubulin multimers complexed with nocodazole and mebendazole, two carbendazim derivatives with potent anticancer activity, highlighted the possibility of designing compounds that occupy both benzimidazole- and colchicine-binding sites. In addition, previous studies have demonstrated that the incorporation of a phenoxy group at position 5/6 of carbendazim increases the antiproliferative activity in cancer cell lines. Despite the significant progress made in identifying new tubulin-targeting anticancer compounds, further modifications are needed to enhance their potency and safety. In this study, we explored the impact of modifying the phenoxy substitution pattern on antiproliferative activity. Alchemical free energy calculations were used to predict the binding free energy difference upon ligand modification and define the most viable path for structure optimization. Based on these calculations, seven compounds were synthesized and evaluated against lung and colon cancer cell lines. Our results showed that compound 5a, which incorporates an α-naphthyloxy substitution, exhibits the highest antiproliferative activity against both cancer lines (SK-LU-1 and SW620, IC50 < 100 nM) and induces morphological changes in the cells associated with mitotic arrest and mitotic catastrophe. Nevertheless, the tubulin polymerization assay showed that 5a has a lower inhibitory potency than nocodazole. Molecular dynamics simulations suggested that this low antitubulin activity could be associated with the loss of the key H-bond interaction with V236. This study provides insights into the design of novel carbendazim derivatives with anticancer activity.
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Affiliation(s)
- Lucia Cano-González
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Johan D Espinosa-Mendoza
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Félix Matadamas-Martínez
- Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, UMAE Hospital de Pediatría, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico
| | - Ariana Romero-Velásquez
- Departamento de Biología, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Miguel Flores-Ramos
- Escuela Nacional de Estudios Superiores, Unidad Mérida, Universidad Nacional Autónoma de México, Yucatán 97357, Mexico
| | - Luis Fernando Colorado-Pablo
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | | | - Rafael Castillo
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Ignacio González-Sánchez
- Departamento de Biología, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Lilián Yépez-Mulia
- Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, UMAE Hospital de Pediatría, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico
| | - Alicia Hernández-Campos
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Rodrigo Aguayo-Ortiz
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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25
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Ojha AA, Votapka LW, Amaro RE. QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations. Chem Sci 2023; 14:13159-13175. [PMID: 38023523 PMCID: PMC10664576 DOI: 10.1039/d3sc04195f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/22/2023] [Indexed: 12/01/2023] Open
Abstract
Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor-ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor-ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor-ligand unbinding kinetics obtained using the newly reparameterized force fields for model host-guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor-ligand koff rate constants.
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Affiliation(s)
- Anupam Anand Ojha
- Department of Chemistry and Biochemistry, University of California San Diego La Jolla California 92093 USA
| | - Lane William Votapka
- Department of Chemistry and Biochemistry, University of California San Diego La Jolla California 92093 USA
| | - Rommie Elizabeth Amaro
- Department of Molecular Biology, University of California San Diego La Jolla California 92093 USA
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26
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York DM. Modern Alchemical Free Energy Methods for Drug Discovery Explained. ACS PHYSICAL CHEMISTRY AU 2023; 3:478-491. [PMID: 38034038 PMCID: PMC10683484 DOI: 10.1021/acsphyschemau.3c00033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 12/02/2023]
Abstract
This Perspective provides a contextual explanation of the current state-of-the-art alchemical free energy methods and their role in drug discovery as well as highlights select emerging technologies. The narrative attempts to answer basic questions about what goes on "under the hood" in free energy simulations and provide general guidelines for how to run simulations and analyze the results. It is the hope that this work will provide a valuable introduction to students and scientists in the field.
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Affiliation(s)
- Darrin M. York
- Laboratory for Biomolecular
Simulation Research, Institute for Quantitative Biomedicine, and Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway, New Jersey 08854, United States
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27
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Wan S, Bhati AP, Coveney PV. Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions. J Chem Theory Comput 2023; 19:7846-7860. [PMID: 37862058 PMCID: PMC10653111 DOI: 10.1021/acs.jctc.3c00842] [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] [Received: 08/02/2023] [Indexed: 10/21/2023]
Abstract
Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.
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Affiliation(s)
- Shunzhou Wan
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U.K.
| | - Agastya P. Bhati
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U.K.
| | - Peter V. Coveney
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U.K.
- Advanced
Research Computing Centre, University College
London, London WC1H 0AJ, U.K.
- Computational
Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam 1012 WP, Netherlands
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28
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Papadourakis M, Sinenka H, Matricon P, Hénin J, Brannigan G, Pérez-Benito L, Pande V, van Vlijmen H, de Graaf C, Deflorian F, Tresadern G, Cecchini M, Cournia Z. Alchemical Free Energy Calculations on Membrane-Associated Proteins. J Chem Theory Comput 2023; 19:7437-7458. [PMID: 37902715 PMCID: PMC11017255 DOI: 10.1021/acs.jctc.3c00365] [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] [Received: 03/31/2023] [Indexed: 10/31/2023]
Abstract
Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.
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Affiliation(s)
- Michail Papadourakis
- Biomedical
Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
| | - Hryhory Sinenka
- Institut
de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, F-67083 Strasbourg Cedex, France
| | - Pierre Matricon
- Sosei
Heptares, Steinmetz Building,
Granta Park, Great Abington, Cambridge CB21 6DG, United
Kingdom
| | - Jérôme Hénin
- Laboratoire
de Biochimie Théorique UPR 9080, CNRS and Université Paris Cité, 75005 Paris, France
| | - Grace Brannigan
- Center
for Computational and Integrative Biology, Rutgers University−Camden, Camden, New Jersey 08103, United States of America
- Department
of Physics, Rutgers University−Camden, Camden, New Jersey 08102, United States
of America
| | - Laura Pérez-Benito
- CADD,
In Silico Discovery, Janssen Research &
Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Vineet Pande
- CADD,
In Silico Discovery, Janssen Research &
Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Herman van Vlijmen
- CADD,
In Silico Discovery, Janssen Research &
Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Chris de Graaf
- Sosei
Heptares, Steinmetz Building,
Granta Park, Great Abington, Cambridge CB21 6DG, United
Kingdom
| | - Francesca Deflorian
- Sosei
Heptares, Steinmetz Building,
Granta Park, Great Abington, Cambridge CB21 6DG, United
Kingdom
| | - Gary Tresadern
- CADD,
In Silico Discovery, Janssen Research &
Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Marco Cecchini
- Institut
de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, F-67083 Strasbourg Cedex, France
| | - Zoe Cournia
- Biomedical
Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
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29
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Wilson C, Karttunen M, de Groot BL, Gapsys V. Accurately Predicting Protein p Ka Values Using Nonequilibrium Alchemy. J Chem Theory Comput 2023; 19:7833-7845. [PMID: 37820376 PMCID: PMC10653114 DOI: 10.1021/acs.jctc.3c00721] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Indexed: 10/13/2023]
Abstract
The stability, solubility, and function of a protein depend on both its net charge and the protonation states of its individual residues. pKa is a measure of the tendency for a given residue to (de)protonate at a specific pH. Although pKa values can be resolved experimentally, theory and computation provide a compelling alternative. To this end, we assess the applicability of a nonequilibrium (NEQ) alchemical free energy method to the problem of pKa prediction. On a data set of 144 residues that span 13 proteins, we report an average unsigned error of 0.77 ± 0.09, 0.69 ± 0.09, and 0.52 ± 0.04 pK for aspartate, glutamate, and lysine, respectively. This is comparable to current state-of-the-art predictors and the accuracy recently reached using free energy perturbation methods (e.g., FEP+). Moreover, we demonstrate that our open-source, pmx-based approach can accurately resolve the pKa values of coupled residues and observe a substantial performance disparity associated with the lysine partial charges in Amber14SB/Amber99SB*-ILDN, for which an underused fix already exists.
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Affiliation(s)
- Carter
J. Wilson
- Department
of Mathematics, The University of Western
Ontario, N6A 5B7 London, Canada
- Centre
for Advanced Materials and Biomaterials Research (CAMBR), The University of Western Ontario, N6A 5B7 London, Canada
| | - Mikko Karttunen
- Centre
for Advanced Materials and Biomaterials Research (CAMBR), The University of Western Ontario, N6A 5B7 London, Canada
- Department
of Physics & Astronomy, The University
of Western Ontario, N6A
5B7 London, Canada
- Department
of Chemistry, The University of Western
Ontario, N6A 5B7 London, Canada
| | - Bert L. de Groot
- Computational
Biomolecular Dynamics Group, Department of Theoretical and Computational
Biophysics, Max Planck Institute for Multidisciplinary
Sciences, 37077 Göttingen, Germany
| | - Vytautas Gapsys
- Computational
Biomolecular Dynamics Group, Department of Theoretical and Computational
Biophysics, Max Planck Institute for Multidisciplinary
Sciences, 37077 Göttingen, Germany
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
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30
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Bobrovs R, Drunka L, Kanepe I, Jirgensons A, Caflisch A, Salvalaglio M, Jaudzems K. Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors. J Chem Inf Model 2023; 63:6890-6899. [PMID: 37801405 DOI: 10.1021/acs.jcim.3c00826] [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: 10/08/2023]
Abstract
Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.
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Affiliation(s)
- Raitis Bobrovs
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Laura Drunka
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Iveta Kanepe
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Aigars Jirgensons
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Matteo Salvalaglio
- Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom
| | - Kristaps Jaudzems
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
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31
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Boby ML, Fearon D, Ferla M, Filep M, Koekemoer L, Robinson MC, Chodera JD, Lee AA, London N, von Delft A, von Delft F, Achdout H, Aimon A, Alonzi DS, Arbon R, Aschenbrenner JC, Balcomb BH, Bar-David E, Barr H, Ben-Shmuel A, Bennett J, Bilenko VA, Borden B, Boulet P, Bowman GR, Brewitz L, Brun J, Bvnbs S, Calmiano M, Carbery A, Carney DW, Cattermole E, Chang E, Chernyshenko E, Clyde A, Coffland JE, Cohen G, Cole JC, Contini A, Cox L, Croll TI, Cvitkovic M, De Jonghe S, Dias A, Donckers K, Dotson DL, Douangamath A, Duberstein S, Dudgeon T, Dunnett LE, Eastman P, Erez N, Eyermann CJ, Fairhead M, Fate G, Fedorov O, Fernandes RS, Ferrins L, Foster R, Foster H, Fraisse L, Gabizon R, García-Sastre A, Gawriljuk VO, Gehrtz P, Gileadi C, Giroud C, Glass WG, Glen RC, Glinert I, Godoy AS, Gorichko M, Gorrie-Stone T, Griffen EJ, Haneef A, Hassell Hart S, Heer J, Henry M, Hill M, Horrell S, Huang QYJ, Huliak VD, Hurley MFD, Israely T, Jajack A, Jansen J, Jnoff E, Jochmans D, John T, Kaminow B, Kang L, Kantsadi AL, Kenny PW, Kiappes JL, Kinakh SO, Kovar B, Krojer T, La VNT, Laghnimi-Hahn S, Lefker BA, Levy H, Lithgo RM, Logvinenko IG, Lukacik P, Macdonald HB, MacLean EM, Makower LL, Malla TR, Marples PG, Matviiuk T, McCorkindale W, McGovern BL, Melamed S, Melnykov KP, Michurin O, Miesen P, Mikolajek H, Milne BF, Minh D, Morris A, Morris GM, Morwitzer MJ, Moustakas D, Mowbray CE, Nakamura AM, Neto JB, Neyts J, Nguyen L, Noske GD, Oleinikovas V, Oliva G, Overheul GJ, Owen CD, Pai R, Pan J, Paran N, Payne AM, Perry B, Pingle M, Pinjari J, Politi B, Powell A, Pšenák V, Pulido I, Puni R, Rangel VL, Reddi RN, Rees P, Reid SP, Reid L, Resnick E, Ripka EG, Robinson RP, Rodriguez-Guerra J, Rosales R, Rufa DA, Saar K, Saikatendu KS, Salah E, Schaller D, Scheen J, Schiffer CA, Schofield CJ, Shafeev M, Shaikh A, Shaqra AM, Shi J, Shurrush K, Singh S, Sittner A, Sjö P, Skyner R, Smalley A, Smeets B, Smilova MD, Solmesky LJ, Spencer J, Strain-Damerell C, Swamy V, Tamir H, Taylor JC, Tennant RE, Thompson W, Thompson A, Tomásio S, Tomlinson CWE, Tsurupa IS, Tumber A, Vakonakis I, van Rij RP, Vangeel L, Varghese FS, Vaschetto M, Vitner EB, Voelz V, Volkamer A, Walsh MA, Ward W, Weatherall C, Weiss S, White KM, Wild CF, Witt KD, Wittmann M, Wright N, Yahalom-Ronen Y, Yilmaz NK, Zaidmann D, Zhang I, Zidane H, Zitzmann N, Zvornicanin SN. Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors. Science 2023; 382:eabo7201. [PMID: 37943932 PMCID: PMC7615835 DOI: 10.1126/science.abo7201] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 10/09/2023] [Indexed: 11/12/2023]
Abstract
We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.
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Affiliation(s)
- Melissa L Boby
- Pharmacology Graduate Program, Weill Cornell Graduate School of Medical Sciences, New York, NY 10065, USA
- Program in Chemical Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Program in Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Daren Fearon
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, UK
| | - Matteo Ferla
- Oxford Biomedical Research Centre, National Institute for Health Research, University of Oxford, Oxford, UK
| | - Mihajlo Filep
- Department of Chemical and Structural Biology, The Weizmann Institute of Science, Rehovot, Israel
| | - Lizbé Koekemoer
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - John D Chodera
- Program in Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Nir London
- Department of Chemical and Structural Biology, The Weizmann Institute of Science, Rehovot, Israel
| | - Annette von Delft
- Oxford Biomedical Research Centre, National Institute for Health Research, University of Oxford, Oxford, UK
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Frank von Delft
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, UK
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Hagit Achdout
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Anthony Aimon
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Dominic S Alonzi
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
| | - Robert Arbon
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
| | - Jasmin C Aschenbrenner
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Blake H Balcomb
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Elad Bar-David
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Haim Barr
- The Weizmann Institute of Science, Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Rehovot, 7610001, Israel
| | - Amir Ben-Shmuel
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - James Bennett
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
- University of Oxford, Nuffield Department of Medicine, Target Discovery Institute, Oxford, OX3 7FZ, UK
| | - Vitaliy A Bilenko
- Enamine Ltd, Kyiv, 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv, 01601, Ukraine
| | | | - Pascale Boulet
- Drugs for Neglected Diseases Initiative (DNDi), Geneva, 1202, Switzerland
| | - Gregory R Bowman
- University of Pennsylvania, Departments of Biochemistry and Biophysics and Bioengineering, Philadelphia, PA 19083, USA
| | - Lennart Brewitz
- University of Oxford, Department of Chemistry, Chemistry Research Laboratory, Oxford, OX1 3TA, UK
| | - Juliane Brun
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
| | - Sarma Bvnbs
- Sai Life Sciences Limited, ICICI Knowledge Park, Shameerpet, Hyderabad 500 078, Telangana, India
| | | | - Anna Carbery
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- University of Oxford, Department of Statistics, Oxford OX1 3LB, UK
| | - Daniel W Carney
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | - Emma Cattermole
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
| | - Edcon Chang
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | | | | | | | - Galit Cohen
- The Weizmann Institute of Science, Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Rehovot, 7610001, Israel
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, CB2 1EZ, UK
| | - Alessandro Contini
- University of Milan, Department of General and Organic Chemistry, Milan, 20133, Italy
| | - Lisa Cox
- Life Compass Consulting Ltd, Macclesfield, SK10 5UE, UK
| | - Tristan Ian Croll
- The University of Cambridge, Cambridge Institute for Medical Research, Department of Haematology, Cambridge CB2 0XY, UK
- Present address: Altos Labs, BioML group, Great Abington, CB21 6GP
| | | | - Steven De Jonghe
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven, Belgium
| | - Alex Dias
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Kim Donckers
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven, Belgium
| | | | - Alice Douangamath
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Shirly Duberstein
- The Weizmann Institute of Science, Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Rehovot, 7610001, Israel
| | - Tim Dudgeon
- Informatics Matters Ltd, Bicester, OX26 6JU, UK
| | - Louise E Dunnett
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Peter Eastman
- Stanford University, Department of Chemistry, Stanford, CA 94305, USA
| | - Noam Erez
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Charles J Eyermann
- Northeastern University, Department of Chemistry and Chemical Biology, Boston MA 02115, USA
| | - Michael Fairhead
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
| | - Gwen Fate
- Thames Pharma Partners LLC, Mystic, CT 06355, USA
| | - Oleg Fedorov
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
- University of Oxford, Nuffield Department of Medicine, Target Discovery Institute, Oxford, OX3 7FZ, UK
| | - Rafaela S Fernandes
- University of Sao Paulo, Sao Carlos Institute of Physics, Sao Carlos, 13563-120, Brazil
| | - Lori Ferrins
- Northeastern University, Department of Chemistry and Chemical Biology, Boston MA 02115, USA
| | - Richard Foster
- University of Leeds, School of Chemistry, Leeds, LS2 9JT, UK
| | - Holly Foster
- University of Leeds, School of Chemistry, Leeds, LS2 9JT, UK
- Present address: Exscientia, Oxford Science Park, Oxford, OX4 4GE, UK
| | - Laurent Fraisse
- Drugs for Neglected Diseases Initiative (DNDi), Geneva, 1202, Switzerland
| | - Ronen Gabizon
- The Weizmann Institute of Science, Department of Chemical and Structural Biology, Rehovot, 7610001, Israel
| | - Adolfo García-Sastre
- Icahn School of Medicine at Mount Sinai, Department of Microbiology, New York, NY 10029, USA
- Icahn School of Medicine at Mount Sinai, Global Health and Emerging Pathogens Institute, New York, NY 10029, USA
- Icahn School of Medicine at Mount Sinai, Department of Medicine, Division of Infectious Diseases, New York, NY 10029, USA
- Icahn School of Medicine at Mount Sinai, The Tisch Cancer Institute, New York, NY 10029, USA
- Icahn School of Medicine at Mount Sinai, Department of Pathology, Molecular and Cell-Based Medicine, New York, NY 10029, USA
| | - Victor O Gawriljuk
- University of Sao Paulo, Sao Carlos Institute of Physics, Sao Carlos, 13563-120, Brazil
- Present address: University of Groningen, Groningen Research Institute of Pharmacy, Department of Drug Design, Groningen, 9700 AV, Netherlands
| | - Paul Gehrtz
- The Weizmann Institute of Science, Department of Chemical and Structural Biology, Rehovot, 7610001, Israel
- Present address: Merck Healthcare KGaA, Darmstadt, 64293, Germany
| | - Carina Gileadi
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
| | - Charline Giroud
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
- University of Oxford, Nuffield Department of Medicine, Target Discovery Institute, Oxford, OX3 7FZ, UK
| | - William G Glass
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
- Present address: Exscientia, Oxford Science Park, Oxford, OX4 4GE, UK
| | - Robert C Glen
- University of Cambridge, Department of Chemistry, Cambridge, CB2 1EW, UK
| | - Itai Glinert
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Andre S Godoy
- University of Sao Paulo, Sao Carlos Institute of Physics, Sao Carlos, 13563-120, Brazil
| | - Marian Gorichko
- Taras Shevchenko National University of Kyiv, Kyiv, 01601, Ukraine
| | - Tyler Gorrie-Stone
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Ed J Griffen
- MedChemica Ltd, Macclesfield, Cheshire. SK11 6PU UK
| | - Amna Haneef
- Illinois Institute of Technology, Department of Biology, Chicago IL 60616 USA
| | - Storm Hassell Hart
- University of Sussex, Department of Chemistry, School of Life Sciences, Brighton, East Sussex, BN1 9QJ, UK
| | - Jag Heer
- Syngene International Limited, Headington, Oxford, OX3 7BZ, UK
| | - Michael Henry
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
| | - Michelle Hill
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
- Present address: Sir William Dunn School of Pathology, Oxford. OX1 3RE, UK
| | - Sam Horrell
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Qiu Yu Judy Huang
- University of Massachusetts, Chan Medical School, Department of Biochemistry and Molecular Biotechnology, Worcester MA 01655, USA
| | | | | | - Tomer Israely
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | | | - Jitske Jansen
- RWTH Aachen University, Institute of Experimental Medicine and Systems Biology, Aachen, 52074, Germany
| | - Eric Jnoff
- UCB, Chemin du Foriest, 1420 Braine-l'Alleud, Belgium
| | - Dirk Jochmans
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven, Belgium
| | - Tobias John
- University of Oxford, Department of Chemistry, Chemistry Research Laboratory, Oxford, OX1 3TA, UK
- Present address: AMSilk, Neuried, 82061, Germany
| | - Benjamin Kaminow
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Tri-Institutional Program in Computational Biology and Medicine, New York, NY 10065, USA
| | - Lulu Kang
- Illinois Institute of Technology, Department of Applied Mathematics, Chicago IL 60616 USA
| | - Anastassia L Kantsadi
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
- University of Thessaly, Department of Biochemistry and Biotechnology, Larissa, 415 00, Greece
| | - Peter W Kenny
- Berwick-on-Sea, North Coast Road, Blanchisseuse, Saint George, Trinidad and Tobago
| | - J L Kiappes
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
- Present address: University College of London, Department of Chemistry, London WC1H 0AJ, UK
| | | | - Boris Kovar
- M2M solutions s.r.o. Žilina, 010 01, Slovakia
| | - Tobias Krojer
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
- MAX IV Laboratory, Fotongatan 2, 224 84 Lund, Sweden
| | - Van Ngoc Thuy La
- Illinois Institute of Technology, Department of Biology, Chicago IL 60616 USA
| | | | | | - Haim Levy
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Ryan M Lithgo
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | | | - Petra Lukacik
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Hannah Bruce Macdonald
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
- Present address: Charm Therapeutics, London, N1C 4AG, UK
| | - Elizabeth M MacLean
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
| | - Laetitia L Makower
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
| | - Tika R Malla
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
| | - Peter G Marples
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | | | - Willam McCorkindale
- Present address: Charm Therapeutics, London, N1C 4AG, UK
- University of Cambridge, Cavendish Laboratory, Cambridge, CB3 0HE UK
| | - Briana L McGovern
- Icahn School of Medicine at Mount Sinai, Department of Microbiology, New York, NY 10029, USA
- Icahn School of Medicine at Mount Sinai, Global Health and Emerging Pathogens Institute, New York, NY 10029, USA
| | - Sharon Melamed
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Kostiantyn P Melnykov
- Enamine Ltd, Kyiv, 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv, 01601, Ukraine
| | | | - Pascal Miesen
- Radboud University Medical Center, Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Nijmegen, 6525 GA, Netherlands
| | - Halina Mikolajek
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Bruce F Milne
- University of Aberdeen, Department of Chemistry, Old Aberdeen, AB24 3UE Scotland, UK
- University of Coimbra, CFisUC, Department of Physics, Coimbra, 3004-516, Portugal
| | - David Minh
- Illinois Institute of Technology, Department of Chemistry, Chicago IL 60616 USA
| | | | - Garrett M Morris
- University of Oxford, Department of Statistics, Oxford OX1 3LB, UK
| | - Melody Jane Morwitzer
- University of Nebraska Medical Centre, Dept of Pathology and Microbiology, Omaha, NE 68198-5900, USA
| | | | - Charles E Mowbray
- Drugs for Neglected Diseases Initiative (DNDi), Geneva, 1202, Switzerland
| | - Aline M Nakamura
- University of Sao Paulo, Sao Carlos Institute of Physics, Sao Carlos, 13563-120, Brazil
- Present address: Instituto Butantan, Sao Paulo, 05503-900, Brazil
| | - Jose Brandao Neto
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Johan Neyts
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven, Belgium
| | | | - Gabriela D Noske
- University of Sao Paulo, Sao Carlos Institute of Physics, Sao Carlos, 13563-120, Brazil
| | - Vladas Oleinikovas
- UCB, Slough, SL1 3WE, UK
- Present address: Monte Rosa Therapeutics, Basel, CH 4057, Switzerland
| | - Glaucius Oliva
- University of Sao Paulo, Sao Carlos Institute of Physics, Sao Carlos, 13563-120, Brazil
| | - Gijs J Overheul
- Radboud University Medical Center, Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Nijmegen, 6525 GA, Netherlands
| | - C David Owen
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Ruby Pai
- PostEra Inc., Cambridge, MA, 02142, USA
| | - Jin Pan
- PostEra Inc., Cambridge, MA, 02142, USA
| | - Nir Paran
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Alexander Matthew Payne
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Tri-Institutional Program in Computational Biology and Medicine, New York, NY 10065, USA
| | - Benjamin Perry
- Drugs for Neglected Diseases Initiative (DNDi), Geneva, 1202, Switzerland
- Present address: Medicxi, Geneva, 1204, Switzerland
| | - Maneesh Pingle
- Sai Life Sciences Limited, ICICI Knowledge Park, Shameerpet, Hyderabad 500 078, Telangana, India
| | - Jakir Pinjari
- Sai Life Sciences Limited, ICICI Knowledge Park, Shameerpet, Hyderabad 500 078, Telangana, India
- Present address: Sun Pharma Advanced Research Company (SPARC), Baroda, India
| | - Boaz Politi
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Ailsa Powell
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | | | - Iván Pulido
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
| | - Reut Puni
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Victor L Rangel
- University of São Paulo, Ribeirão Preto School of Pharmaceutical Sciences, Ribeirão Preto - SP/CEP 14040-903, Brazil
- Present address: Evotec (UK) Ltd, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Rambabu N Reddi
- The Weizmann Institute of Science, Department of Chemical and Structural Biology, Rehovot, 7610001, Israel
| | - Paul Rees
- Compass Bussiness Partners Ltd, Southcliffe, Bucks, SL9 0PD, UK
| | - St Patrick Reid
- University of Nebraska Medical Centre, Dept of Pathology and Microbiology, Omaha, NE 68198-5900, USA
| | - Lauren Reid
- MedChemica Ltd, Macclesfield, Cheshire. SK11 6PU UK
| | - Efrat Resnick
- The Weizmann Institute of Science, Department of Chemical and Structural Biology, Rehovot, 7610001, Israel
| | | | | | - Jaime Rodriguez-Guerra
- Charité - Universitätsmedizin Berlin, In silico Toxicology and Structural Bioinformatics, Berlin, 10117, Germany
| | - Romel Rosales
- Icahn School of Medicine at Mount Sinai, Department of Microbiology, New York, NY 10029, USA
- Icahn School of Medicine at Mount Sinai, Global Health and Emerging Pathogens Institute, New York, NY 10029, USA
| | - Dominic A Rufa
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Tri-Institutional Program in Computational Biology and Medicine, New York, NY 10065, USA
| | - Kadi Saar
- University of Cambridge, Cavendish Laboratory, Cambridge, CB3 0HE UK
| | | | - Eidarus Salah
- University of Oxford, Department of Chemistry, Chemistry Research Laboratory, Oxford, OX1 3TA, UK
| | - David Schaller
- Charité - Universitätsmedizin Berlin, In silico Toxicology and Structural Bioinformatics, Berlin, 10117, Germany
| | - Jenke Scheen
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
| | - Celia A Schiffer
- University of Massachusetts, Chan Medical School, Department of Biochemistry and Molecular Biotechnology, Worcester MA 01655, USA
| | - Christopher J Schofield
- University of Oxford, Department of Chemistry, Chemistry Research Laboratory, Oxford, OX1 3TA, UK
| | | | - Aarif Shaikh
- Sai Life Sciences Limited, ICICI Knowledge Park, Shameerpet, Hyderabad 500 078, Telangana, India
| | - Ala M Shaqra
- University of Massachusetts, Chan Medical School, Department of Biochemistry and Molecular Biotechnology, Worcester MA 01655, USA
| | - Jiye Shi
- UCB, Chemin du Foriest, 1420 Braine-l'Alleud, Belgium
- Present address: Eli Lilly and Company, San Diego, CA 92121, USA
| | - Khriesto Shurrush
- The Weizmann Institute of Science, Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Rehovot, 7610001, Israel
| | - Sukrit Singh
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
| | - Assa Sittner
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Peter Sjö
- Drugs for Neglected Diseases Initiative (DNDi), Geneva, 1202, Switzerland
| | - Rachael Skyner
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | | | - Bart Smeets
- Radboud University Medical Center, Department of pathology, Radboud Institute for Molecular Life Sciences, Nijmegen, 6525 GA, Netherlands
| | - Mihaela D Smilova
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
| | - Leonardo J Solmesky
- The Weizmann Institute of Science, Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Rehovot, 7610001, Israel
| | - John Spencer
- University of Sussex, Department of Chemistry, School of Life Sciences, Brighton, East Sussex, BN1 9QJ, UK
| | - Claire Strain-Damerell
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Vishwanath Swamy
- Sai Life Sciences Limited, ICICI Knowledge Park, Shameerpet, Hyderabad 500 078, Telangana, India
- Present address: TCG Life Sciences, Pune, India
| | - Hadas Tamir
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Jenny C Taylor
- University of Oxford, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, Oxford OX3 7BN, UK
| | | | - Warren Thompson
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Andrew Thompson
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
- Present address: Walter and Eliza Hall Institute, Parkville 3052, Victoria, Australia
| | | | - Charles W E Tomlinson
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | | | - Anthony Tumber
- University of Oxford, Department of Chemistry, Chemistry Research Laboratory, Oxford, OX1 3TA, UK
| | - Ioannis Vakonakis
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
- Present address: Lonza Biologics, Lonza Ltd, Lonzastrasse, CH-3930 Visp, Switzerland
| | - Ronald P van Rij
- Radboud University Medical Center, Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Nijmegen, 6525 GA, Netherlands
| | - Laura Vangeel
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven, Belgium
| | - Finny S Varghese
- Radboud University Medical Center, Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Nijmegen, 6525 GA, Netherlands
- Present address: uniQure Biopharma, Amsterdam, 1105 BP, Netherlands
| | | | - Einat B Vitner
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Vincent Voelz
- Temple University, Department of Chemistry, Philadelphia, PA 19122, USA
| | - Andrea Volkamer
- Charité - Universitätsmedizin Berlin, In silico Toxicology and Structural Bioinformatics, Berlin, 10117, Germany
- Present address: Saarland University, Data Driven Drug Design, Campus - E2.1, 66123 Saarbrücken, Germany
| | - Martin A Walsh
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Walter Ward
- Walter Ward Consultancy and Training, Derbyshire, SK22 4AA, UK
| | | | - Shay Weiss
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Kris M White
- Icahn School of Medicine at Mount Sinai, Department of Microbiology, New York, NY 10029, USA
- Icahn School of Medicine at Mount Sinai, Global Health and Emerging Pathogens Institute, New York, NY 10029, USA
| | - Conor Francis Wild
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Karolina D Witt
- University of Oxford, Nuffield Department of Medicine, Pandemic Sciences Institute, Oxford, Oxon, OX3 7DQ, UK
| | - Matthew Wittmann
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
| | - Nathan Wright
- University of Oxford, Nuffield Department of Medicine, Centre for Medicines Discovery, Oxford, OX3 7DQ, UK
| | - Yfat Yahalom-Ronen
- Israel Institute for Biological Research, Department of Infectious Diseases, Ness-Ziona, Israel
| | - Nese Kurt Yilmaz
- University of Massachusetts, Chan Medical School, Department of Biochemistry and Molecular Biotechnology, Worcester MA 01655, USA
| | - Daniel Zaidmann
- The Weizmann Institute of Science, Department of Chemical and Structural Biology, Rehovot, 7610001, Israel
| | - Ivy Zhang
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, NY 10065, USA
| | - Hadeer Zidane
- The Weizmann Institute of Science, Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Rehovot, 7610001, Israel
| | - Nicole Zitzmann
- University of Oxford, Department of Biochemistry, Oxford Glycobiology Institute, South Parks Road, Oxford OX1 3QU, UK
| | - Sarah N Zvornicanin
- University of Massachusetts, Chan Medical School, Department of Biochemistry and Molecular Biotechnology, Worcester MA 01655, USA
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Libouban PY, Aci-Sèche S, Gómez-Tamayo JC, Tresadern G, Bonnet P. The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks. Int J Mol Sci 2023; 24:16120. [PMID: 38003312 PMCID: PMC10671244 DOI: 10.3390/ijms242216120] [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] [Received: 09/14/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023] Open
Abstract
Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein-ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models' decision-making processes and accurately compare the performance of models.
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Affiliation(s)
- Pierre-Yves Libouban
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
| | - Samia Aci-Sèche
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
| | - Jose Carlos Gómez-Tamayo
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., B-2340 Beerse, Belgium; (J.C.G.-T.); (G.T.)
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., B-2340 Beerse, Belgium; (J.C.G.-T.); (G.T.)
| | - Pascal Bonnet
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
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33
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Li J, Luo S, Ouyang X, Wu G, Deng Z, He X, Zhao YL. Understanding base and backbone contributions of phosphorothioate DNA for molecular recognition with SBD proteins. Phys Chem Chem Phys 2023; 25:29289-29302. [PMID: 37876253 DOI: 10.1039/d3cp02820h] [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: 10/26/2023]
Abstract
Bacterial DNA phosphorothioate (PT) modification provides a specific anchoring site for sulfur-binding proteins (SBDs). Besides, their recognition patterns include phosphate links and bases neighboring the PT-modified site, thereby bringing about genome sequence-dependent properties in PT-related epigenetics. Here, we analyze the contributions of the DNA backbone (phosphates and deoxyribose) and bases bound with two SBD proteins in Streptomyces pristinaespiralis and coelicolor (SBDSco and SBDSpr). The chalcogen-hydrophobic interactions remained constantly at the anchoring site while the adjacent bases formed conditional and distinctive non-covalent interactions. More importantly, SBD/PT-DNA interactions were not limited within the traditional "4-bp core" range from 5'-I to 3'-III but extended to upstream 5'-II and 5'-III bases and even 5''-I to 5''-III at the non-PT-modified complementary strand. From the epigenetic viewpoint, bases 3'-II, 5''-I, and 5''-III of SBDSpr and 3'-II, 5''-II, and 5''-III of SBDSco present remarkable differentiations in the molecular recognitions. From the protein viewpoint, H102 in SBDSpr and R191 in SBDSco contribute significantly while proline residues at the PT-bound site are strictly conserved for the PT-chalcogen bond. The mutual and make-up mutations are proposed to alter the SBD/PT-DNA recognition pattern, besides additional chiral phosphorothioate modifications on phosphates 5'-II, 5'-II, 3'-I, and 3'-II.
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Affiliation(s)
- Jiayi Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Shenggan Luo
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xingyu Ouyang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Geng Wu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Zixin Deng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xinyi He
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
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34
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Velasco-Saavedra MA, Mar-Antonio E, Aguayo-Ortiz R. Molecular Insights into the Covalent Binding of Zoxamide to the β-Tubulin of Botrytis cinerea. J Chem Inf Model 2023; 63:6386-6395. [PMID: 37802126 DOI: 10.1021/acs.jcim.3c00911] [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: 10/08/2023]
Abstract
Botrytis cinerea is a fungal plant pathogen that causes significant economic losses in the agricultural industry worldwide. Fungicides that target microtubules, such as carbendazim (CBZ), diethofencarb (DEF), and zoxamide (ZOX), are widely used in crop protection against this pathogen. These groups of compounds exert their fungicidal activity by disrupting the microtubule assembly by binding to the β-tubulin subunit, provoking cell-cycle arrest and cell death. However, with the appearance of isolates resistant to these compounds, it is necessary to search for new alternatives to control this pathogenic fungus. In this work, we gained insight into the binding and stability of these fungicides in the benzimidazole binding site of B. cinerea β-tubulin through different computational approaches. Our molecular dynamics simulation replicas showed that R enantiomers of ZOX and its analog RH-4032 had better interaction profiles at the site compared to S enantiomers. The simulations also revealed that while the R-isomer fungicides formed H-bonds with the main chain carbonyl of V236 or the side chain residue of S314, only CBZ interacted with E198. Previous experimental data have identified key mutations in B. cinerea's β-tubulin gene that lead to the development of resistance or, on the contrary, increased sensitivity for treatment with these fungicide compounds. In agreement with experimental findings, alchemical free energy calculations showed that E198A and E198V mutations in B. cinerea β-tubulin have high sensitivity to (R)-ZOX, whereas the E198K mutation decreased its affinity. Similarly, the results obtained explain the resistance to CBZ of B. cinerea isolates with E198A/V/K mutations and the insensitivity of the wild-type organism to DEF. Our work provides a deeper insight into the molecular mechanism of action of these fungicides, highlighting the importance of understanding the interaction profiles to develop more effective antifungal agents.
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Affiliation(s)
- M Andrés Velasco-Saavedra
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Efrén Mar-Antonio
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Rodrigo Aguayo-Ortiz
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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35
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Ross GA, Lu C, Scarabelli G, Albanese SK, Houang E, Abel R, Harder ED, Wang L. The maximal and current accuracy of rigorous protein-ligand binding free energy calculations. Commun Chem 2023; 6:222. [PMID: 37838760 PMCID: PMC10576784 DOI: 10.1038/s42004-023-01019-9] [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: 10/18/2022] [Accepted: 10/02/2023] [Indexed: 10/16/2023] Open
Abstract
Computational techniques can speed up the identification of hits and accelerate the development of candidate molecules for drug discovery. Among techniques for predicting relative binding affinities, the most consistently accurate is free energy perturbation (FEP), a class of rigorous physics-based methods. However, uncertainty remains about how accurate FEP is and can ever be. Here, we present what we believe to be the largest publicly available dataset of proteins and congeneric series of small molecules, and assess the accuracy of the leading FEP workflow. To ascertain the limit of achievable accuracy, we also survey the reproducibility of experimental relative affinity measurements. We find a wide variability in experimental accuracy and a correspondence between binding and functional assays. When careful preparation of protein and ligand structures is undertaken, FEP can achieve accuracy comparable to experimental reproducibility. Throughout, we highlight reliable protocols that can help maximize the accuracy of FEP in prospective studies.
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Affiliation(s)
- Gregory A Ross
- Schrödinger Inc, New York, NY, USA.
- Isomorphic Labs, London, UK.
| | - Chao Lu
- Schrödinger Inc, New York, NY, USA
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36
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Lundborg M, Lidmar J, Hess B. On the Path to Optimal Alchemistry. Protein J 2023; 42:477-489. [PMID: 37651042 PMCID: PMC10480267 DOI: 10.1007/s10930-023-10137-1] [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] [Accepted: 07/04/2023] [Indexed: 09/01/2023]
Abstract
Alchemical free energy calculations have become a standard and widely used tool, in particular for calculating and comparing binding affinities of drugs. Although methods to compute such free energies have improved significantly over the last decades, the choice of path between the end states of interest is usually still the same as two decades ago. We will show that there is a fundamentally arbitrary, implicit choice of parametrization of this path. To address this, the notion of the length of a path or a metric is required. A metric recently introduced in the context of the accelerated weight histogram method also proves to be very useful here. We demonstrate that this metric can not only improve the efficiency of sampling along a given path, but that it can also be used to improve the actual choice of path. For a set of relevant use cases, the combination of these improvements can increase the efficiency of alchemical free energy calculations by up to a factor 16.
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Affiliation(s)
| | - Jack Lidmar
- Department of Physics, KTH Royal Institute of Technology, 10691, Stockholm, Sweden
| | - Berk Hess
- Department of Applied Physics, KTH Royal Institute of Technology, 10691, Stockholm, Science for Life Laboratory, Solna, Sweden.
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37
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Bello M, Bandala C. Evaluating the ability of end-point methods to predict the binding affinity tendency of protein kinase inhibitors. RSC Adv 2023; 13:25118-25128. [PMID: 37614784 PMCID: PMC10443623 DOI: 10.1039/d3ra04916g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023] Open
Abstract
Because of the high economic cost of exploring the experimental impact of mutations occurring in kinase proteins, computational approaches have been employed as alternative methods for evaluating the structural and energetic aspects of kinase mutations. Among the main computational methods used to explore the affinity linked to kinase mutations are docking procedures and molecular dynamics (MD) simulations combined with end-point methods or alchemical methods. Although it is known that end-point methods are not able to reproduce experimental binding free energy (ΔG) values, it is also true that they are able to discriminate between a better or a worse ligand through the estimation of ΔG. In this contribution, we selected ten wild-type and mutant cocrystallized EGFR-inhibitor complexes containing experimental binding affinities to evaluate whether MMGBSA or MMPBSA approaches can predict the differences in affinity between the wild type and mutants forming a complex with a similar inhibitor. Our results show that a long MD simulation (the last 50 ns of a 100 ns-long MD simulation) using the MMGBSA method without considering the entropic components reproduced the experimental affinity tendency with a Pearson correlation coefficient of 0.779 and an R2 value of 0.606. On the other hand, the correlation between theoretical and experimental ΔΔG values indicates that the MMGBSA and MMPBSA methods are helpful for obtaining a good correlation using a short rather than a long simulation period.
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Affiliation(s)
- Martiniano Bello
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Diaz Mirón s/n, Col. Casco de Santo Tomas Ciudad de México 11340 Mexico
| | - Cindy Bandala
- Escuela Superior de Medicina, Instituto Politécnico Nacional México City 11340 Mexico
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38
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Silvestri G, Arrigoni F, Persico F, Bertini L, Zampella G, De Gioia L, Vertemara J. Assessing the Performance of Non-Equilibrium Thermodynamic Integration in Flavodoxin Redox Potential Estimation. Molecules 2023; 28:6016. [PMID: 37630271 PMCID: PMC10459689 DOI: 10.3390/molecules28166016] [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] [Received: 06/30/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Flavodoxins are enzymes that contain the redox-active flavin mononucleotide (FMN) cofactor and play a crucial role in numerous biological processes, including energy conversion and electron transfer. Since the redox characteristics of flavodoxins are significantly impacted by the molecular environment of the FMN cofactor, the evaluation of the interplay between the redox properties of the flavin cofactor and its molecular surroundings in flavoproteins is a critical area of investigation for both fundamental research and technological advancements, as the electrochemical tuning of flavoproteins is necessary for optimal interaction with redox acceptor or donor molecules. In order to facilitate the rational design of biomolecular devices, it is imperative to have access to computational tools that can accurately predict the redox potential of both natural and artificial flavoproteins. In this study, we have investigated the feasibility of using non-equilibrium thermodynamic integration protocols to reliably predict the redox potential of flavodoxins. Using as a test set the wild-type flavodoxin from Clostridium Beijerinckii and eight experimentally characterized single-point mutants, we have computed their redox potential. Our results show that 75% (6 out of 8) of the calculated reaction free energies are within 1 kcal/mol of the experimental values, and none exceed an error of 2 kcal/mol, confirming that non-equilibrium thermodynamic integration is a trustworthy tool for the quantitative estimation of the redox potential of this biologically and technologically significant class of enzymes.
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Affiliation(s)
| | | | | | | | | | - Luca De Gioia
- Department of Biotechnology and Biosciences BtBs, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Jacopo Vertemara
- Department of Biotechnology and Biosciences BtBs, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
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39
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Baumann H, Dybeck E, McClendon CL, Pickard FC, Gapsys V, Pérez-Benito L, Hahn DF, Tresadern G, Mathiowetz AM, Mobley DL. Broadening the Scope of Binding Free Energy Calculations Using a Separated Topologies Approach. J Chem Theory Comput 2023; 19:5058-5076. [PMID: 37487138 PMCID: PMC10413862 DOI: 10.1021/acs.jctc.3c00282] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Indexed: 07/26/2023]
Abstract
Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.
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Affiliation(s)
- Hannah
M. Baumann
- Department
of Pharmaceutical Sciences, University of
California, Irvine, Irvine, California 92697, United States
| | - Eric Dybeck
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Christopher L. McClendon
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Frank C. Pickard
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Vytautas Gapsys
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Laura Pérez-Benito
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - David F. Hahn
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Gary Tresadern
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Alan M. Mathiowetz
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - David L. Mobley
- Department
of Pharmaceutical Sciences, University of
California, Irvine, Irvine, California 92697, United States
- Department
of Chemistry, University of California,
Irvine, Irvine, California 92697, United States
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40
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Kopec W, Thomson AS, de Groot BL, Rothberg BS. Interactions between selectivity filter and pore helix control filter gating in the MthK channel. J Gen Physiol 2023; 155:e202213166. [PMID: 37318452 PMCID: PMC10274084 DOI: 10.1085/jgp.202213166] [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] [Received: 03/29/2022] [Revised: 01/13/2023] [Accepted: 05/31/2023] [Indexed: 06/16/2023] Open
Abstract
K+ channel activity can be limited by C-type inactivation, which is likely initiated in part by dissociation of K+ ions from the selectivity filter and modulated by the side chains that surround it. While crystallographic and computational studies have linked inactivation to a "collapsed" selectivity filter conformation in the KcsA channel, the structural basis for selectivity filter gating in other K+ channels is less clear. Here, we combined electrophysiological recordings with molecular dynamics simulations, to study selectivity filter gating in the model potassium channel MthK and its V55E mutant (analogous to KcsA E71) in the pore-helix. We found that MthK V55E has a lower open probability than the WT channel, due to decreased stability of the open state, as well as a lower unitary conductance. Simulations account for both of these variables on the atomistic scale, showing that ion permeation in V55E is altered by two distinct orientations of the E55 side chain. In the "vertical" orientation, in which E55 forms a hydrogen bond with D64 (as in KcsA WT channels), the filter displays reduced conductance compared to MthK WT. In contrast, in the "horizontal" orientation, K+ conductance is closer to that of MthK WT; although selectivity filter stability is lowered, resulting in more frequent inactivation. Surprisingly, inactivation in MthK WT and V55E is associated with a widening of the selectivity filter, unlike what is observed for KcsA and reminisces recent structures of inactivated channels, suggesting a conserved inactivation pathway across the potassium channel family.
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Affiliation(s)
- Wojciech Kopec
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Andrew S. Thomson
- Department of Medical Genetics and Molecular Biochemistry, Temple University Lewis Katz School of Medicine, Philadelphia, PA, USA
| | - Bert L. de Groot
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Brad S. Rothberg
- Department of Medical Genetics and Molecular Biochemistry, Temple University Lewis Katz School of Medicine, Philadelphia, PA, USA
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41
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Rusina P, Gandalipov E, Abdusheva Y, Panova M, Burdenkova A, Chaliy V, Brachs M, Stroganov O, Guzeeva K, Svitanko I, Shtil A, Novikov F. Imidazole-4-N-acetamide Derivatives as a Novel Scaffold for Selective Targeting of Cyclin Dependent Kinases. Cancers (Basel) 2023; 15:3766. [PMID: 37568583 PMCID: PMC10417023 DOI: 10.3390/cancers15153766] [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: 05/31/2023] [Revised: 07/16/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
The rational design of cyclin-dependent protein kinase (CDK) inhibitors presumes the development of approaches for accurate prediction of selectivity and the activity of small molecular weight anticancer drug candidates. Aiming at attenuation of general toxicity of low selectivity compounds, we herein explored the new chemotype of imidazole-4-N-acetamide substituted derivatives of the pan-CDK inhibitor PHA-793887. Newly synthesized compounds 1-4 containing an aliphatic methyl group or aromatic radicals at the periphery of the scaffold were analyzed for the prediction of relative free energies of binding to CDK1, -2, -5, and -9 using a protocol based on non-equilibrium (NEQ) thermodynamics. This methodology allows for the demonstration of a good correlation between the calculated parameters of interaction of 1-4 with individual targets and the values of inhibitory potencies in in vitro kinase assays. We provide evidence in support of NEQ thermodynamics as a time sparing, precise, and productive approach for generating chemical inhibitors of clinically relevant anticancer targets.
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Affiliation(s)
- Polina Rusina
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Avenue, 119991 Moscow, Russia
| | - Erik Gandalipov
- Laboratory of Solution Chemistry and Advanced Materials Technologies, ITMO University, 9 Lomonosov Street, 191002 Saint Petersburg, Russia
- PHARMENTERPRISES LLC, Skolkovo Innovation Center, 42 (1) Bolshoi Blvd., 143026 Moscow, Russia
| | - Yana Abdusheva
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Avenue, 119991 Moscow, Russia
- PHARMENTERPRISES LLC, Skolkovo Innovation Center, 42 (1) Bolshoi Blvd., 143026 Moscow, Russia
- Higher School of Economics, National Research University, 20 Myasnitskaya Street, 101000 Moscow, Russia
| | - Maria Panova
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Avenue, 119991 Moscow, Russia
- PHARMENTERPRISES LLC, Skolkovo Innovation Center, 42 (1) Bolshoi Blvd., 143026 Moscow, Russia
| | - Alexandra Burdenkova
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Avenue, 119991 Moscow, Russia
- Higher School of Economics, National Research University, 20 Myasnitskaya Street, 101000 Moscow, Russia
| | - Vasiliy Chaliy
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Avenue, 119991 Moscow, Russia
| | - Maria Brachs
- Treamid Therapeutics GmbH, c/o CoLaborator (Bayer), Building S141, Muellerstraβe 178, 13353 Berlin, Germany
| | | | - Ksenia Guzeeva
- Higher School of Economics, National Research University, 20 Myasnitskaya Street, 101000 Moscow, Russia
| | - Igor Svitanko
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Avenue, 119991 Moscow, Russia
- Higher School of Economics, National Research University, 20 Myasnitskaya Street, 101000 Moscow, Russia
| | - Alexander Shtil
- Blokhin National Medical Research Center of Oncology, 24 Kashirskoye Shosse, 115522 Moscow, Russia
- Institute of Cyber Intelligence Systems, National Research Nuclear University MEPhI, 31 Kashirskoye Shosse, 115409 Moscow, Russia
| | - Fedor Novikov
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 47 Leninsky Avenue, 119991 Moscow, Russia
- PHARMENTERPRISES LLC, Skolkovo Innovation Center, 42 (1) Bolshoi Blvd., 143026 Moscow, Russia
- Higher School of Economics, National Research University, 20 Myasnitskaya Street, 101000 Moscow, Russia
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42
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Boothroyd S, Behara PK, Madin OC, Hahn DF, Jang H, Gapsys V, Wagner JR, Horton JT, Dotson DL, Thompson MW, Maat J, Gokey T, Wang LP, Cole DJ, Gilson MK, Chodera JD, Bayly CI, Shirts MR, Mobley DL. Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field. J Chem Theory Comput 2023; 19:3251-3275. [PMID: 37167319 PMCID: PMC10269353 DOI: 10.1021/acs.jctc.3c00039] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Indexed: 05/13/2023]
Abstract
We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.
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Affiliation(s)
| | - Pavan Kumar Behara
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California 92697, United States
| | - Owen C. Madin
- Chemical
& Biological Engineering Department, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - David F. Hahn
- Computational
Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Hyesu Jang
- Chemistry
Department, The University of California
at Davis, Davis, California 95616, United States
- OpenEye
Scientific Software, Santa
Fe, New Mexico 87508, United States
| | - Vytautas Gapsys
- Computational
Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
- Computational
Biomolecular Dynamics Group, Department of Theoretical and Computational
Biophysics, Max Planck Institute for Multidisciplinary
Sciences, Am Fassberg 11, D-37077, Göttingen, Germany
| | - Jeffrey R. Wagner
- Department
of Pharmaceutical Sciences, University of
California, Irvine, California 92697, United States
- The Open
Force Field Initiative, Open Molecular Software
Foundation, Davis, California 95616, United States
| | - Joshua T. Horton
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, U.K.
| | - David L. Dotson
- The Open
Force Field Initiative, Open Molecular Software
Foundation, Davis, California 95616, United States
- Datryllic LLC, Phoenix, Arizona 85003, United
States
| | - Matthew W. Thompson
- Chemical
& Biological Engineering Department, University of Colorado Boulder, Boulder, Colorado 80309, United States
- The Open
Force Field Initiative, Open Molecular Software
Foundation, Davis, California 95616, United States
| | - Jessica Maat
- Department
of Chemistry, University of California, Irvine, California 92697, United States
| | - Trevor Gokey
- Department
of Chemistry, University of California, Irvine, California 92697, United States
| | - Lee-Ping Wang
- Chemistry
Department, The University of California
at Davis, Davis, California 95616, United States
| | - Daniel J. Cole
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, U.K.
| | - Michael K. Gilson
- Skaggs
School of Pharmacy and Pharmaceutical Sciences, The University of California at San Diego, La Jolla, California 92093, United States
| | - John D. Chodera
- Computational
& Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | | | - Michael R. Shirts
- Chemical
& Biological Engineering Department, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - 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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43
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Chen W, Cui D, Jerome SV, Michino M, Lenselink EB, Huggins DJ, Beautrait A, Vendome J, Abel R, Friesner RA, Wang L. Enhancing Hit Discovery in Virtual Screening through Absolute Protein-Ligand Binding Free-Energy Calculations. J Chem Inf Model 2023; 63:3171-3185. [PMID: 37167486 DOI: 10.1021/acs.jcim.3c00013] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
In the hit identification stage of drug discovery, a diverse chemical space needs to be explored to identify initial hits. Contrary to empirical scoring functions, absolute protein-ligand binding free-energy perturbation (ABFEP) provides a theoretically more rigorous and accurate description of protein-ligand binding thermodynamics and could, in principle, greatly improve the hit rates in virtual screening. In this work, we describe an implementation of an accurate and reliable ABFEP method in FEP+. We validated the ABFEP method on eight congeneric compound series binding to eight protein receptors including both neutral and charged ligands. For ligands with net charges, the alchemical ion approach is adopted to avoid artifacts in electrostatic potential energy calculations. The calculated binding free energies correlate with experimental results with a weighted average of R2 = 0.55 for the entire dataset. We also observe an overall root-mean-square error (RMSE) of 1.1 kcal/mol after shifting the zero-point of the simulation data to match the average experimental values. Through ABFEP calculations using apo versus holo protein structures, we demonstrated that the protein conformational and protonation state changes between the apo and holo proteins are the main physical factors contributing to the protein reorganization free energy manifested by the overestimation of raw ABFEP calculated binding free energies using the holo structures of the proteins. Furthermore, we performed ABFEP calculations in three virtual screening applications for hit enrichment. ABFEP greatly improves the hit rates as compared to docking scores or other methods like metadynamics. The good performance of ABFEP in rank ordering compounds demonstrated in this work confirms it as a useful tool to improve the hit rates in virtual screening, thus facilitating hit discovery.
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Affiliation(s)
- Wei Chen
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Di Cui
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Steven V Jerome
- Schrödinger, Inc., 10201 Wateridge Circle, Suite 220, San Diego, California 92121, United States
| | - Mayako Michino
- Tri-Institutional Therapeutics Discovery Institute, 413 E. 69th Street, New York, New York 10065, United States
| | | | - David J Huggins
- Tri-Institutional Therapeutics Discovery Institute, 413 E. 69th Street, New York, New York 10065, United States
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, New York 10065, United States
| | - Alexandre Beautrait
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Jeremie Vendome
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University, New York, New York 10027, United States
| | - Lingle Wang
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
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44
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Molani F, Webb S, Cho AE. Combining QM/MM Calculations with Classical Mining Minima to Predict Protein-Ligand Binding Free Energy. J Chem Inf Model 2023; 63:2728-2734. [PMID: 37079618 DOI: 10.1021/acs.jcim.2c01637] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
We developed an effective binding free energy prediction protocol which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations to substitute the specified atomic charges of force fields with quantum-mechanically recalculated ones at a proposed pose using a mining minima approach with the VeraChem mining minima engine. We tested this protocol using seven well-known targets with 147 different ligands and compared it with classical mining minima and the most popular binding free energy (BFE) methods using different metrics. Our new protocol, dubbed Qcharge-VM2, yielded an overall Pearson correlation of 0.86, which was better than all the methods examined. Qcharge-VM2 performed significantly better than implicit solvent-based methods, such as MM-GBSA and MM-PBSA, but not as good as explicit water-based free energy perturbation methods, such as FEP+, in terms of root-mean-square error, RMSE (1.75 kcal/mol) and mean unsigned error, MUE (1.39 kcal/mol) on a limited set of targets. However, our protocol is substantially less computationally demanding compared with FEP+. The combined accuracy and efficiency of our method can be valuable in drug discovery campaigns.
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Affiliation(s)
- Farzad Molani
- Department of Bioinformatics, Korea University, 2511 Sejong-ro, Sejong 30119, Korea
| | - Simon Webb
- VeraChem LLC, 12850 Middlebrook Road STE 205, Germantown, Maryland 20874, United States
| | - Art E Cho
- Department of Bioinformatics, Korea University, 2511 Sejong-ro, Sejong 30119, Korea
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45
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Moore JH, Margreitter C, Janet JP, Engkvist O, de Groot BL, Gapsys V. Automated relative binding free energy calculations from SMILES to ΔΔG. Commun Chem 2023; 6:82. [PMID: 37106032 PMCID: PMC10140266 DOI: 10.1038/s42004-023-00859-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/22/2023] [Indexed: 04/29/2023] Open
Abstract
In drug discovery, computational methods are a key part of making informed design decisions and prioritising experiments. In particular, optimizing compound affinity is a central concern during the early stages of development. In the last 10 years, alchemical free energy (FE) calculations have transformed our ability to incorporate accurate in silico potency predictions in design decisions, and represent the 'gold standard' for augmenting experiment-driven drug discovery. However, relative FE calculations are complex to set up, require significant expert intervention to prepare the calculation and analyse the results or are provided only as closed-source software, not allowing for fine-grained control over the underlying settings. In this work, we introduce an end-to-end relative FE workflow based on the non-equilibrium switching approach that facilitates calculation of binding free energies starting from SMILES strings. The workflow is implemented using fully modular steps, allowing various components to be exchanged depending on licence availability. We further investigate the dependence of the calculated free energy accuracy on the initial ligand pose generated by various docking algorithms. We show that both commercial and open-source docking engines can be used to generate poses that lead to good correlation of free energies with experimental reference data.
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Affiliation(s)
- J Harry Moore
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | | | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, D-37077, Göttingen, Germany.
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, D-37077, Göttingen, Germany.
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46
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Sabanés Zariquiey F, Pérez A, Majewski M, Gallicchio E, De Fabritiis G. Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series. J Chem Inf Model 2023; 63:2438-2444. [PMID: 37042797 PMCID: PMC10577236 DOI: 10.1021/acs.jcim.3c00178] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology. In this study, we evaluate the performance of a relative binding free energy protocol based on the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The results show that ATM matches the performance of more complex free energy perturbation (FEP) methods in terms of Pearson correlation but with marginally higher mean absolute errors. This study shows that the ATM method is competitive compared to more traditional methods in speed and accuracy and offers the advantage of being applicable with any potential energy function.
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Affiliation(s)
- Francesc Sabanés Zariquiey
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Adrià Pérez
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | | | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, United States
- PhD Program in Chemistry Graduate Center of the City University of New York, New York, New York 10016, United States
- PhD Program in Biochemistry, Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera, Devonshire House 582 Honeypot Lane, Stanmore, Middlesex HA7 1JS, United Kingdom
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
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47
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Parui S, Robertson JC, Somani S, Tresadern G, Liu C, Dill KA. MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands. J Chem Inf Model 2023; 63:2857-2865. [PMID: 37093848 DOI: 10.1021/acs.jcim.3c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.
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Affiliation(s)
- Sridip Parui
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - James C Robertson
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Sandeep Somani
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Gary Tresadern
- Janssen Research and Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Cong Liu
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
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48
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Yoo J, Kim TY, Joung I, Song SO. Industrializing AI/ML during the end-to-end drug discovery process. Curr Opin Struct Biol 2023; 79:102528. [PMID: 36736243 DOI: 10.1016/j.sbi.2023.102528] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 02/04/2023]
Abstract
Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.
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Affiliation(s)
- Jiho Yoo
- Standigm Inc., 3F, 70 Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, South Korea, 06234 +82.2.501.8118
| | - Tae Yong Kim
- Standigm Inc., 3F, 70 Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, South Korea, 06234 +82.2.501.8118
| | - InSuk Joung
- Standigm Inc., 3F, 70 Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, South Korea, 06234 +82.2.501.8118
| | - Sang Ok Song
- Standigm Inc., 3F, 70 Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, South Korea, 06234 +82.2.501.8118.
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49
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Pitman M, Hahn DF, Tresadern G, Mobley DL. To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough. J Chem Inf Model 2023; 63:1776-1793. [PMID: 36878475 PMCID: PMC10547263 DOI: 10.1021/acs.jcim.2c01579] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the simulation experiment, using graphs where nodes represent ligands and graph edges represent alchemical transformations between ligands. Recent work demonstrated that optimizing the statistical architecture of these perturbation graphs improves the accuracy of the predicted changes in the free energy of ligand binding. Therefore, to improve the success rate of computational drug discovery, we present the open-source software package High Information Mapper (HiMap)─a new take on its predecessor, Lead Optimization Mapper (LOMAP). HiMap removes heuristics decisions from design selection and instead finds statistically optimal graphs over ligands clustered with machine learning. Beyond optimal design generation, we present theoretical insights for designing alchemical perturbation maps. Some of these results include that for n number of nodes, the precision of perturbation maps is stable at n·ln(n) edges. This result indicates that even an "optimal" graph can result in unexpectedly high errors if a plan includes too few alchemical transformations for the given number of ligands and edges. And, as a study compares more ligands, the performance of even optimal graphs will deteriorate with linear scaling of the edge count. In this sense, ensuring an A- or D-optimal topology is not enough to produce robust errors. We additionally find that optimal designs will converge more rapidly than radial and LOMAP designs. Moreover, we derive bounds for how clustering reduces cost for designs with a constant expected relative error per cluster, invariant of the size of the design. These results inform how to best design perturbation maps for computational drug discovery and have broader implications for experimental design.
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Affiliation(s)
- Mary Pitman
- Department of Pharmacy & Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - David L. Mobley
- Department of Pharmacy & Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
- Department of Chemistry, University of California, Irvine, CA 92697, USA
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50
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Nakamura S, Akaki T, Nishiwaki K, Nakatani M, Kawase Y, Takahashi Y, Nakanishi I. System truncation accelerates binding affinity calculations with the fragment molecular orbital method: A benchmark study. J Comput Chem 2023; 44:824-831. [PMID: 36444861 DOI: 10.1002/jcc.27044] [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: 08/07/2022] [Revised: 11/01/2022] [Accepted: 11/03/2022] [Indexed: 11/30/2022]
Abstract
The fragment molecular orbital (FMO) method is a fast quantum-mechanical method that divides systems into pieces of fragments and performs ab initio calculations. The system truncation enables further speed improvement. In this article, we systematically study the effects of system truncations on binding affinity calculations obtained with FMO in combination with either the polarizable continuum model (FMO/PCM) or in combination with the Møller-Plesset method (FMO-MP2). We have used five protein complexes with ligands of several charged states. The calculated binding energies of the size variants of the truncated system, including only a restricted number of atoms around the ligand, are compared to the energy obtained from a full system. The result shows that the systems could be truncated to a radius of 8 Å from neutral ligands within an error of 0.7 kcal/mol, and 12 Å from charged ligands within an error of 1.1 kcal/mol for calculating the binding energy in solution.
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Affiliation(s)
- Shinya Nakamura
- Computational Drug Design and Discovery, Department of Pharmaceutical Sciences, Kindai University, Osaka, Japan
| | - Tatsuo Akaki
- Computational Drug Design and Discovery, Department of Pharmaceutical Sciences, Kindai University, Osaka, Japan.,Chemical Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., Osaka, Japan
| | - Keiji Nishiwaki
- Computational Drug Design and Discovery, Department of Pharmaceutical Sciences, Kindai University, Osaka, Japan
| | - Midori Nakatani
- Computational Drug Design and Discovery, Department of Pharmaceutical Sciences, Kindai University, Osaka, Japan
| | - Yuji Kawase
- Computational Drug Design and Discovery, Department of Pharmaceutical Sciences, Kindai University, Osaka, Japan
| | - Yuki Takahashi
- Computational Drug Design and Discovery, Department of Pharmaceutical Sciences, Kindai University, Osaka, Japan
| | - Isao Nakanishi
- Computational Drug Design and Discovery, Department of Pharmaceutical Sciences, Kindai University, Osaka, Japan
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