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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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2
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Champion C, Hünenberger PH, Riniker S. Multistate Method to Efficiently Account for Tautomerism and Protonation in Alchemical Free-Energy Calculations. J Chem Theory Comput 2024; 20:4350-4362. [PMID: 38742760 PMCID: PMC11137823 DOI: 10.1021/acs.jctc.4c00370] [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: 03/22/2024] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/16/2024]
Abstract
The majority of drug-like molecules contain at least one ionizable group, and many common drug scaffolds are subject to tautomeric equilibria. Thus, these compounds are found in a mixture of protonation and/or tautomeric states at physiological pH. Intrinsically, standard classical molecular dynamics (MD) simulations cannot describe such equilibria between states, which negatively impacts the prediction of key molecular properties in silico. Following the formalism described by de Oliveira and co-workers (J. Chem. Theory Comput. 2019, 15, 424-435) to consider the influence of all states on the binding process based on alchemical free-energy calculations, we demonstrate in this work that the multistate method replica-exchange enveloping distribution sampling (RE-EDS) is well suited to describe molecules with multiple protonation and/or tautomeric states in a single simulation. We apply our methodology to a series of eight inhibitors of factor Xa with two protonation states and a series of eight inhibitors of glycogen synthase kinase 3β (GSK3β) with two tautomeric states. In particular, we show that given a sufficient phase-space overlap between the states, RE-EDS is computationally more efficient than standard pairwise free-energy methods.
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Affiliation(s)
- Candide Champion
- Department of Chemistry and Applied
Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Philippe H. Hünenberger
- 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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3
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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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4
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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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5
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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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6
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Hasan MN, Ray M, Saha A. Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery. J Phys Chem B 2023; 127:9663-9684. [PMID: 37921534 DOI: 10.1021/acs.jpcb.3c04710] [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: 11/04/2023]
Abstract
Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.
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Affiliation(s)
- Md Nazmul Hasan
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
| | - Manisha Ray
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Arjun Saha
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
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