1
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Awoonor-Williams E, Abu-Saleh AAAA. Molecular Insights into the Impact of Mutations on the Binding Affinity of Targeted Covalent Inhibitors of BTK. J Phys Chem B 2024; 128:2874-2884. [PMID: 38502552 DOI: 10.1021/acs.jpcb.4c00310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Targeted covalent inhibitors (TCIs) have witnessed a significant resurgence in recent years, particularly in the kinase drug discovery field for treating diverse clinical indications. The inhibition of Bruton's tyrosine kinase (BTK) for treating B-cell cancers is a classic example where TCIs such as ibrutinib have had breakthroughs in targeted therapy. However, selectivity remains challenging, and the emergence of resistance mutations is a critical concern for clinical efficacy. Computational methods that can accurately predict the impact of mutations on inhibitor binding affinity could prove helpful in informing targeted approaches─providing insights into drug resistance mechanisms. In addition, such systems could help guide the systematic evaluation and impact of mutations in disease models for optimal experimental design. Here, we have employed in silico physics-based methods to understand the effects of mutations on the binding affinity and conformational dynamics of select TCIs of BTK. The TCIs studied include ibrutinib, acalabrutinib, and zanubrutinib─all of which are FDA-approved drugs for treating multiple forms of leukemia and lymphoma. Our results offer useful molecular insights into the structural determinants, thermodynamics, and conformational energies that impact ligand binding for this biological target of clinical relevance.
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Affiliation(s)
- Ernest Awoonor-Williams
- Department of Chemistry, Memorial University of Newfoundland, St. John's, NL A1B 3X7, Canada
| | - Abd Al-Aziz A Abu-Saleh
- Department of Chemistry and Biochemistry, University of Windsor, Windsor, ON N9B 3P4, Canada
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2
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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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3
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Karrenbrock M, Rizzi V, Procacci P, Gervasio FL. Addressing Suboptimal Poses in Nonequilibrium Alchemical Calculations. J Phys Chem B 2024; 128:1595-1605. [PMID: 38323915 DOI: 10.1021/acs.jpcb.3c06516] [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: 02/08/2024]
Abstract
Alchemical transformations can be used to quantitatively estimate absolute binding free energies at a reasonable computational cost. However, most of the approaches currently in use require knowledge of the correct (crystallographic) pose. In this paper, we present a combined Hamiltonian replica exchange nonequilibrium alchemical method that allows us to reliably calculate absolute binding free energies, even when starting from suboptimal initial binding poses. Performing a preliminary Hamiltonian replica exchange enhances the sampling of slow degrees of freedom of the ligand and the target, allowing the system to populate the correct binding pose when starting from an approximate docking pose. We apply the method on 6 ligands of the first bromodomain of the BRD4 bromodomain-containing protein. For each ligand, we start nonequilibrium alchemical transformations from both the crystallographic pose and the top-scoring docked pose that are often significantly different. We show that the method produces statistically equivalent binding free energies, making it a useful tool for computational drug discovery pipelines.
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Affiliation(s)
- Maurice Karrenbrock
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, CH-1206 Geneva, Switzerland
| | - Valerio Rizzi
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, CH-1206 Geneva, Switzerland
| | - Piero Procacci
- Chemistry Department, University of Florence, Via della Lastruccia 3-13, 50019 Sesto Fiorentino, Italy
| | - Francesco Luigi Gervasio
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, CH-1206 Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CH-1206 Geneva, Switzerland
- Chemistry Department, University College London (UCL), WC1E 6BT London, U.K
- Swiss Bioinformatics Institute, University of Geneva, CH-1206 Geneva, Switzerland
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4
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Smith L, Novak B, Osato M, Mobley DL, Bowman GR. PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model. J Chem Theory Comput 2024; 20:1036-1050. [PMID: 38291966 PMCID: PMC10867841 DOI: 10.1021/acs.jctc.3c00870] [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: 08/08/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 02/01/2024]
Abstract
Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.
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Affiliation(s)
- Louis
G. Smith
- Departments
of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Borna Novak
- Department
of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Medical
Scientist Training Program, Washington University
in St. Louis, St. Louis, Missouri 63130, United
States
| | - Meghan Osato
- School
of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - David L. Mobley
- School
of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - Gregory R. Bowman
- Departments
of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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5
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Setiadi J, Boothroyd S, Slochower DR, Dotson DL, Thompson MW, Wagner JR, Wang LP, Gilson MK. Tuning Potential Functions to Host-Guest Binding Data. J Chem Theory Comput 2024; 20:239-252. [PMID: 38147689 PMCID: PMC10838530 DOI: 10.1021/acs.jctc.3c01050] [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: 12/28/2023]
Abstract
Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.
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Affiliation(s)
- Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States
| | - Simon Boothroyd
- Boothroyd Scientific Consulting Ltd., London WC2H 9JQ, U.K
- Psivant Therapeutics, Boston, Massachusetts 02210, United States
| | | | - David L Dotson
- Datryllic LLC, Phoenix, Arizona 85003, United States
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Matthew W Thompson
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Jeffrey R Wagner
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Lee-Ping Wang
- Chemistry Department, University of California Davis, Davis, California 95616, United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States
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6
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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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7
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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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8
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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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9
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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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10
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Ghahremanpour MM, Saar A, Tirado-Rives J, Jorgensen WL. Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease. J Chem Inf Model 2023; 63:5309-5318. [PMID: 37561001 DOI: 10.1021/acs.jcim.3c00874] [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: 08/11/2023]
Abstract
Accurate, routine calculation of absolute binding free energies (ABFEs) for protein-ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (Mpro) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 μs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.
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Affiliation(s)
| | - Anastasia Saar
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Julian Tirado-Rives
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - William L Jorgensen
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
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11
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. J Chem Theory Comput 2023; 19:4863-4882. [PMID: 37450482 DOI: 10.1021/acs.jctc.3c00333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.
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Affiliation(s)
- Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, New York 10065, United States
| | - Dominic A Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, New York 10065, United States
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Michael M Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Laura E Rosen
- Vir Biotechnology, San Francisco, California 94158, United States
| | - Kevin Hauser
- Vir Biotechnology, San Francisco, California 94158, United States
| | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
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12
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Zhou F, Yin S, Xiao Y, Lin Z, Fu W, Zhang YJ. Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation. ACS OMEGA 2023; 8:18312-18322. [PMID: 37251166 PMCID: PMC10210189 DOI: 10.1021/acsomega.3c02294] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (koff) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the koff value. We observed a high correlation among the simulated, predicted, and experimental -log(koff) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our koff predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental koff values and are not in our ML training dataset. The predicted koff values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict koff of other proteins and will enhance the kinetics-based drug design endeavor.
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13
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Macchiagodena M, Pagliai M, Procacci P. NE-RDFE: A protocol and toolkit for computing relative dissociation free energies with GROMACS between dissimilar molecules using bidirectional nonequilibrium dual topology schemes. J Comput Chem 2023; 44:1221-1230. [PMID: 36704972 DOI: 10.1002/jcc.27077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/20/2022] [Accepted: 01/07/2023] [Indexed: 01/28/2023]
Abstract
We describe a step-by-step protocol and toolkit for the computation of the relative dissociation free energy (RDFE) with the GROMACS molecular dynamics package, based on a novel bidirectional nonequilibrium alchemical approach. The proposed methodology does not require any intervention on the code and allows computing with good accuracy the RDFE between small molecules with arbitrary differences in volume, charge, and chemical topology. The procedure is illustrated for the challenging SAMPL9 batch of host-guest pairs. The article is supplemented by a detailed online tutorial, available at https://procacci.github.io/vdssb_gromacs/NE-RDFE and by a public Zenodo repository available at https://zenodo.org/record/6982932.
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Affiliation(s)
- Marina Macchiagodena
- Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Sesto Fiorentino, Italy
| | - Marco Pagliai
- Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Sesto Fiorentino, Italy
| | - Piero Procacci
- Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Sesto Fiorentino, Italy
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14
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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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15
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.530278. [PMID: 36945557 PMCID: PMC10028896 DOI: 10.1101/2023.03.07.530278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated opensource relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
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16
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Sun Z, Zheng L, Zhang ZY, Cong Y, Wang M, Wang X, Yang J, Liu Z, Huai Z. Molecular Modelling of Ionic Liquids: Situations When Charge Scaling Seems Insufficient. MOLECULES (BASEL, SWITZERLAND) 2023; 28:molecules28020800. [PMID: 36677859 PMCID: PMC9865557 DOI: 10.3390/molecules28020800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Charge scaling as an effective solution to the experiment-computation disagreement in molecular modelling of ionic liquids (ILs) could bring the computational results close to the experimental reference for various thermodynamic properties. According to the large-scale benchmark calculations of mass density, solvation, and water-ILs transfer-free energies in our series of papers, the charge-scaling factor of 0.8 serves as a near-optimal option generally applicable to most ILs, although a system-dependent parameter adjustment could be attempted for further improved performance. However, there are situations in which such a charge-scaling treatment would fail. Namely, charge scaling cannot really affect the simulation outcome, or minimally perturbs the results that are still far from the experimental value. In such situations, the vdW radius as an additional adjustable parameter is commonly tuned to minimize the experiment-calculation deviation. In the current work, considering two ILs from the quinuclidinium family, we investigate the impacts of this vdW-scaling treatment on the mass density and the solvation/partition thermodynamics in a fashion similar to our previous charge-scaling works, i.e., scanning the vdW-scaling factor and computing physical properties under these parameter sets. It is observed that the mass density exhibits a linear response to the vdW-scaling factor with slopes close to -1.8 g/mL. By further investigating a set of physiochemically relevant temperatures between 288 K and 348 K, we confirm the robustness of the vdW-scaling treatment in the estimation of bulk properties. The best vdW-scaling parameter for mass density would worsen the computation of solvation/partition thermodynamics, and a marginal decrease in the vdW-scaling factor is considered as an intermediate option balancing the reproductions of bulk properties and solvation thermodynamics. These observations could be understood in a way similar to the charge-scaling situation. i.e., overfitting some properties (e.g., mass density) would degrade the accuracy of the other properties (e.g., solvation free energies). Following this principle, the general guideline for applying this vdW-tuning protocol is by using values between the density-derived choice and the solvation/partition-derived solution. The charge and current vdW scaling treatments cover commonly encountered ILs, completing the protocol for accurate modelling of ILs with fixed-charge force fields.
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Affiliation(s)
- Zhaoxi Sun
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Correspondence: (Z.S.); (X.W.); (Z.H.)
| | - Lei Zheng
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Department of Chemistry, New York University, New York, NY 10003, USA
| | - Zuo-Yuan Zhang
- College of Physical Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Yalong Cong
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Mao Wang
- NCS Testing Technology Co., Ltd., No. 13, Gaoliangqiao Xiejie, Beijing 100081, China
| | - Xiaohui Wang
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Beijing Leto Laboratories Co., Ltd., Beijing 100083, China
- Correspondence: (Z.S.); (X.W.); (Z.H.)
| | - Jingjing Yang
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Zhirong Liu
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Zhe Huai
- XtalPi-AI Research Center, 7F, Tower A, Dongsheng Building, No.8, Zhongguancun East Road, Beijing 100083, China
- Correspondence: (Z.S.); (X.W.); (Z.H.)
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17
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Breznik M, Ge Y, Bluck JP, Briem H, Hahn DF, Christ CD, Mortier J, Mobley DL, Meier K. Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods. ChemMedChem 2023; 18:e202200425. [PMID: 36240514 PMCID: PMC9868080 DOI: 10.1002/cmdc.202200425] [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: 08/01/2022] [Revised: 10/10/2022] [Indexed: 01/26/2023]
Abstract
Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.
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Affiliation(s)
- Marko Breznik
- Computational Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
| | - Joseph P. Bluck
- Computational Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - Hans Briem
- Computational Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Clara D. Christ
- Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - Jérémie Mortier
- Computational Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - David L. Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA,Department of Chemistry, University of California, Irvine, CA 92697, USA
| | - Katharina Meier
- Computational Life Science Technology Functions, Crop Science, R&D, Bayer AG, 40789 Monheim, Germany
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18
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Arabzadeh H, Walker B, Sperling JM, Acevedo O, Ren P, Yang W, Albrecht-Schönzart TE. Molecular Dynamics and Free Energy Calculations of Dicyclohexano-18-crown-6 Diastereoisomers with Sm 2+, Eu 2+, Dy 2+, Yb 2+, Cf 2+, and Three Halide Salts in Tetrahydrofuran and Acetonitrile Using the AMOEBA Force Field. J Phys Chem B 2022; 126:10721-10731. [PMID: 36508277 PMCID: PMC9999210 DOI: 10.1021/acs.jpcb.2c04613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
With the continual development of lanthanides (Ln) in current technological devices, an efficient separation process is needed that can recover greater amounts of these rare elements. Dicyclohexano-18-crown-6 (DCH18C6) is a crown ether that may be a promising candidate for Ln separation, but additional research is required. As such, molecular dynamics (MD) simulations have been performed on four divalent lanthanide halide salts (Sm2+, Eu2+, Dy2+, and Yb2+) and one divalent actinide halide salt (Cf2+) bound to three diastereoisomers of DCH18C6. Dy2+, Yb2+, Cf2+, DCH18C6, and tetrahydrofuran (THF) solvent were parameterized for the AMOEBA polarizable force field for the first time, whereas existing parameters for Sm2+ and Eu2+ were utilized from our previous efforts. A coordination number (CN) of six for Ln2+/An2+-O solvated in THF indicated that the cations interacted almost entirely with the oxygens of the polyether ring. A CN of one for Ln2+/An2+-N solvated in acetonitrile for systems containing iodide suggested that the N atom of acetonitrile was competitive with I- for cation interactions. Fluctuation between five and six CNs for Dy2+ and Yb2+ suggested that although the cations remained in the polyether ring, the size of the ring may not be an ideal fit as these cations possess comparatively smaller ionic radii. Gibbs binding free energies of Sm2+ in all DCH18C6 diastereoisomers solvated in THF were calculated. The binding free energy of the cis-syn-cis diastereoisomer was the most favorable, followed by cis-anti-cis, and then trans-anti-trans. Finally, two major types of conformation were observed for each diastereoisomer that were related to the electrostatic interactions and charge density of the cations.
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Affiliation(s)
- Hesam Arabzadeh
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306, USA
| | - Brandon Walker
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX 78712, USA
| | - Joseph M. Sperling
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306, USA
| | - Orlando Acevedo
- Department of Chemistry, University of Miami, Coral Gables, FL 33146, USA
| | - Pengyu Ren
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX 78712, USA
| | - Wei Yang
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306, USA
- Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
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19
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Ge Y, Baumann HM, Mobley DL. Absolute Binding Free Energy Calculations for Buried Water Molecules. J Chem Theory Comput 2022; 18:6482-6499. [PMID: 36197451 PMCID: PMC9873352 DOI: 10.1021/acs.jctc.2c00658] [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: 01/27/2023]
Abstract
Water often plays a key role in mediating protein-ligand interactions. Understanding contributions from active-site water molecules to binding thermodynamics of a ligand is important in predicting binding free energies for ligand optimization. In this work, we tested a non-equilibrium switching method for absolute binding free energy calculations on water molecules in binding sites of 13 systems. We discuss the lessons we learned about identified issues that affected our calculations and ways to address them. This work fits with our larger focus on how to do accurate ligand binding free energy calculations when water rearrangements are very slow, such as rearrangements due to ligand modification (as in relative free energy calculations) or ligand binding (as in absolute free energy calculations). The method studied in this work can potentially be used to account for limited water sampling via providing endpoint corrections to free energy calculations using our calculated binding free energy of water.
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Affiliation(s)
- Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, California92697, United States
| | - Hannah M Baumann
- Department of Pharmaceutical Sciences, University of California, Irvine, California92697, United States
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, California92697, United States
- Department of Chemistry, University of California, Irvine, California92697, United States
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20
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Alibay I, Magarkar A, Seeliger D, Biggin PC. Evaluating the use of absolute binding free energy in the fragment optimisation process. Commun Chem 2022; 5:105. [PMID: 36697714 PMCID: PMC9814858 DOI: 10.1038/s42004-022-00721-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/10/2022] [Indexed: 02/01/2023] Open
Abstract
Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.
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Affiliation(s)
- Irfan Alibay
- grid.4991.50000 0004 1936 8948Department of Biochemistry, The University of Oxford, South Parks Road, OX1 3QU Oxford, UK
| | - Aniket Magarkar
- grid.420061.10000 0001 2171 7500Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an de Riß, Germany
| | - Daniel Seeliger
- grid.420061.10000 0001 2171 7500Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an de Riß, Germany ,Present Address: Exscientia Inc, Office 400E, 2125 Biscayne Blvd, Miami, FL 33137 USA
| | - Philip Charles Biggin
- grid.4991.50000 0004 1936 8948Department of Biochemistry, The University of Oxford, South Parks Road, OX1 3QU Oxford, UK
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21
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Comparative Interaction Studies of Quercetin with 2-Hydroxyl-propyl-β-cyclodextrin and 2,6-Methylated-β-cyclodextrin. Molecules 2022; 27:molecules27175490. [PMID: 36080258 PMCID: PMC9458201 DOI: 10.3390/molecules27175490] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/06/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Quercetin (QUE) is a well-known natural product that can exert beneficial properties on human health. However, due to its low solubility its bioavailability is limited. In the present study, we examine whether its formulation with two cyclodextrins (CDs) may enhance its pharmacological profile. Comparative interaction studies of quercetin with 2-hydroxyl-propyl-β-cyclodextrin (2HP-β-CD) and 2,6-methylated cyclodextrin (2,6Me-β-CD) were performed using NMR spectroscopy, DFT calculations, and in silico molecular dynamics (MD) simulations. Using T1 relaxation experiments and 2D DOSY it was illustrated that both cyclodextrin vehicles can host quercetin. Quantum mechanical calculations showed the formation of hydrogen bonds between QUE with 2HP-β-CD and 2,6Μe-β-CD. Six hydrogen bonds are formed ranging between 2 to 2.8 Å with 2HP-β-CD and four hydrogen bonds within 2.8 Å with 2,6Μe-β-CD. Calculations of absolute binding free energies show that quercetin binds favorably to both 2,6Me-β-CD and 2HP-β-CD. MM/GBSA results show equally favorable binding of quercetin in the two CDs. Fluorescence spectroscopy shows moderate binding of quercetin in 2HP-β-CD (520 M−1) and 2,6Me-β-CD (770 M−1). Thus, we propose that both formulations (2HP-β-CD:quercetin, 2,6Me-β-CD:quercetin) could be further explored and exploited as small molecule carriers in biological studies.
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22
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Does Hamiltonian Replica Exchange via Lambda-Hopping Enhance the Sampling in Alchemical Free Energy Calculations? Molecules 2022; 27:molecules27144426. [PMID: 35889299 PMCID: PMC9316500 DOI: 10.3390/molecules27144426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 02/06/2023] Open
Abstract
In the context of computational drug design, we examine the effectiveness of the enhanced sampling techniques in state-of-the-art free energy calculations based on alchemical molecular dynamics simulations. In a paradigmatic molecule with competition between conformationally restrained E and Z isomers whose probability ratio is strongly affected by the coupling with the environment, we compare the so-called λ-hopping technique to the Hamiltonian replica exchange methods assessing their convergence behavior as a function of the enhanced sampling protocols (number of replicas, scaling factors, simulation times). We found that the pure λ-hopping, commonly used in solvation and binding free energy calculations via alchemical free energy perturbation techniques, is ineffective in enhancing the sampling of the isomeric states, exhibiting a pathological dependence on the initial conditions. Correct sampling can be restored in λ-hopping simulation by the addition of a “hot-zone” scaling factor to the λ-stratification (FEP+ approach), provided that the additive hot-zone scaling factors are tuned and optimized using preliminary ordinary replica-exchange simulation of the end-states.
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23
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Wade A, Bhati AP, Wan S, Coveney PV. Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility. J Chem Theory Comput 2022; 18:3972-3987. [PMID: 35609233 PMCID: PMC9202356 DOI: 10.1021/acs.jctc.2c00114] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Indexed: 11/28/2022]
Abstract
The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat a myriad of diseases. In this work, we examine the computation of alchemical relative binding free energies with an eye for assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2, and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2, and NAMD3 alpha. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between them of 0.50 kcal/mol. The correlation between packages is very good, with the lowest Spearman's, Pearson's and Kendall's tau correlation coefficients being 0.92, 0.91, and 0.76, respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.
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Affiliation(s)
- Alexander
D. Wade
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Agastya P. Bhati
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Shunzhou Wan
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Peter V. Coveney
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
- Informatics
Institute, University of Amsterdam, Amsterdam 1098XH, The Netherlands
- Advanced
Research Computing Centre, University College
London, London WC1H 0AJ, UK
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24
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Reif MM, Zacharias M. Improving the Potential of Mean Force and Nonequilibrium Pulling Simulations by Simultaneous Alchemical Modifications. J Chem Theory Comput 2022; 18:3873-3893. [PMID: 35653503 DOI: 10.1021/acs.jctc.1c01194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We present an approach combining alchemical modifications and physical-pathway methods to calculate absolute binding free energies. The employed physical-pathway method is either a stratified umbrella sampling to calculate a potential of mean force or nonequilibrium pulling. We devised two basic approaches: the simultaneous approach (S-approach), where, along the physical unbinding pathway, an alchemical transformation of ligand-protein interactions is installed and deinstalled, and the prior-plus-simultaneous approach (PPS-approach), where, prior to the physical-pathway simulation, an alchemical transformation of ligand-protein interactions is installed in the binding site and deinstalled during the physical-pathway simulation. Using a mutant of T4 lysozyme with a benzene ligand as an example, we show that installation and deinstallation of soft-core interactions concurrent with physical ligand unbinding (S-approach) allow successful potential of mean force calculations and nonequilibrium pulling simulations despite the problems posed by the occluded nature of the lysozyme binding pocket. Good agreement between the potential of the mean-force-based S-approach and double decoupling simulations as well as a remarkable efficiency and accuracy of the nonequilibrium-pulling-based S-approach is found. The latter turned out to be more compute-efficient than the potential of mean force calculation by approximately 70%. Furthermore, we illustrate the merits of reducing ligand-protein interactions prior to potential of mean force calculations using the murine double minute homologue protein MDM2 with a p53-derived peptide ligand (PPS-approach). Here, the problem of breaking strong interactions in the binding pocket is transferred to a prior alchemical transformation that reduces the free-energy barrier between the bound and unbound state in the potential of mean force. Besides, disentangling physical ligand displacement from the deinstallation of ligand-protein interactions was seen to allow a more uniform sampling of distance histograms in the umbrella sampling. In the future, physical ligand unbinding combined with simultaneous alchemical modifications may prove useful in the calculation of protein-protein binding free energies, where sampling problems posed by multiple, possibly sticky interactions and potential steric clashes can thus be reduced.
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Affiliation(s)
- Maria M Reif
- Center for Protein Assemblies (CPA), Physics Department, Chair of Theoretical Biophysics (T38), Technical University of Munich, Ernst-Otto-Fischer-Str. 8, Garching 85748, Germany
| | - Martin Zacharias
- Center for Protein Assemblies (CPA), Physics Department, Chair of Theoretical Biophysics (T38), Technical University of Munich, Ernst-Otto-Fischer-Str. 8, Garching 85748, Germany
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25
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Wieczór M, Genna V, Aranda J, Badia RM, Gelpí JL, Gapsys V, de Groot BL, Lindahl E, Municoy M, Hospital A, Orozco M. Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2022; 13:e1622. [PMID: 35935573 PMCID: PMC9347456 DOI: 10.1002/wcms.1622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/25/2022] [Accepted: 04/28/2022] [Indexed: 06/15/2023]
Abstract
Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics. This article is categorized under:Data Science > Computer Algorithms and ProgrammingData Science > Databases and Expert SystemsMolecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods.
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Affiliation(s)
- Miłosz Wieczór
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Department of Physical ChemistryGdansk University of TechnologyGdańskPoland
| | - Vito Genna
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Juan Aranda
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | | | - Josep Lluís Gelpí
- Barcelona Supercomputing CenterBarcelonaSpain
- Department of Biochemistry and BiomedicineUniversity of BarcelonaBarcelonaSpain
| | - Vytautas Gapsys
- Max Planck Institute for Multidisciplinary SciencesComputational Biomolecular Dynamics GroupGoettingenGermany
| | - Bert L. de Groot
- Max Planck Institute for Multidisciplinary SciencesComputational Biomolecular Dynamics GroupGoettingenGermany
| | - Erik Lindahl
- Department of Applied PhysicsSwedish e‐Science Research Center, KTH Royal Institute of TechnologyStockholmSweden
- Department of Biochemistry and Biophysics, Science for Life LaboratoryStockholm UniversityStockholmSweden
| | | | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Department of Biochemistry and BiomedicineUniversity of BarcelonaBarcelonaSpain
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26
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Kutzner C, Kniep C, Cherian A, Nordstrom L, Grubmüller H, de Groot BL, Gapsys V. GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design. J Chem Inf Model 2022; 62:1691-1711. [PMID: 35353508 PMCID: PMC9006219 DOI: 10.1021/acs.jcim.2c00044] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
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We assess costs and
efficiency of state-of-the-art high-performance
cloud computing and compare the results to traditional on-premises
compute clusters. Our use case is atomistic simulations carried out
with the GROMACS molecular dynamics (MD) toolkit with a particular
focus on alchemical protein–ligand binding free energy calculations.
We set up a compute cluster in the Amazon Web Services (AWS) cloud
that incorporates various different instances with Intel, AMD, and
ARM CPUs, some with GPU acceleration. Using representative biomolecular
simulation systems, we benchmark how GROMACS performs on individual
instances and across multiple instances. Thereby we assess which instances
deliver the highest performance and which are the most cost-efficient
ones for our use case. We find that, in terms of total costs, including
hardware, personnel, room, energy, and cooling, producing MD trajectories
in the cloud can be about as cost-efficient as an on-premises cluster
given that optimal cloud instances are chosen. Further, we find that
high-throughput ligand-screening can be accelerated dramatically by
using global cloud resources. For a ligand screening study consisting
of 19 872 independent simulations or ∼200 μs of
combined simulation trajectory, we made use of diverse hardware available
in the cloud at the time of the study. The computations scaled-up
to reach peak performance using more than 4 000 instances,
140 000 cores, and 3 000 GPUs simultaneously. Our simulation
ensemble finished in about 2 days in the cloud, while weeks would
be required to complete the task on a typical on-premises cluster
consisting of several hundred nodes.
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Affiliation(s)
- Carsten Kutzner
- Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Christian Kniep
- Amazon Development Center Germany, Amazon Web Services, Krausenstr. 38, 10117 Berlin, Germany
| | - Austin Cherian
- Amazon Web Services Singapore Pte Ltd, 23 Church St, #10-01, Singapore 049481
| | - Ludvig Nordstrom
- Amazon Web Services, 60 Holborn Viaduct, London EC1A 2FD, United Kingdom
| | - Helmut Grubmüller
- Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
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27
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Markthaler D, Fleck M, Stankiewicz B, Hansen N. Exploring the Effect of Enhanced Sampling on Protein Stability Prediction. J Chem Theory Comput 2022; 18:2569-2583. [PMID: 35298174 DOI: 10.1021/acs.jctc.1c01012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.
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Affiliation(s)
- Daniel Markthaler
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Maximilian Fleck
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Bartosz Stankiewicz
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Niels Hansen
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
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28
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Khalak Y, Tresadern G, Aldeghi M, Baumann HM, Mobley DL, de Groot BL, Gapsys V. Alchemical absolute protein-ligand binding free energies for drug design. Chem Sci 2021; 12:13958-13971. [PMID: 34760182 PMCID: PMC8549785 DOI: 10.1039/d1sc03472c] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/23/2021] [Indexed: 12/13/2022] Open
Abstract
The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains a challenging endeavour, mostly limited to small model cases. Here, we demonstrate accurate first principles based absolute binding free energy estimates for 128 pharmaceutically relevant targets. We use a novel rigorous method to generate protein-ligand ensembles for the ligand in its decoupled state. Not only do the calculations deliver accurate protein-ligand binding affinity estimates, but they also provide detailed physical insight into the structural determinants of binding. We identify subtle rotamer rearrangements between apo and holo states of a protein that are crucial for binding. When compared to relative binding free energy calculations, obtaining absolute binding free energies is considerably more challenging in large part due to the need to explicitly account for the protein in its apo state. In this work we present several approaches to obtain apo state ensembles for accurate absolute ΔG calculations, thus outlining protocols for prospective application of the methods for drug discovery.
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Affiliation(s)
- Y Khalak
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
| | - G Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. Turnhoutseweg 30 2340 Beerse Belgium
| | - M Aldeghi
- MIT Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - H M Baumann
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
| | - D L Mobley
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
- Department of Chemistry, University of California Irvine CA 92697 USA
| | - B L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
| | - V Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
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29
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Procacci P, Macchiagodena M. On the NS-DSSB unidirectional estimates in the SAMPL6 SAMPLing challenge. J Comput Aided Mol Des 2021; 35:1055-1065. [PMID: 34625885 PMCID: PMC8523005 DOI: 10.1007/s10822-021-00419-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 09/27/2021] [Indexed: 12/03/2022]
Abstract
In the context of the recent SAMPL6 SAMPLing challenge (Rizzi et al. 2020 in J Comput Aided Mol Des 34:601–633) aimed at assessing convergence properties and reproducibility of molecular dynamics binding free energy methodologies, we propose a simple explanation of the severe errors observed in the nonequilibrium switch double-system-single-box (NS-DSSB) approach when using unidirectional estimates. At the same time, we suggest a straightforward and minimal modification of the NS-DSSB protocol for obtaining reliable unidirectional estimates for the process where the ligand is decoupled in the bound state and recoupled in the bulk.
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Affiliation(s)
- Piero Procacci
- Department of Chemistry, University of Florence, Via Lastruccia n. 3, 50019, Sesto Fiorentino, FI, Italy.
| | - Marina Macchiagodena
- Department of Chemistry, University of Florence, Via Lastruccia n. 3, 50019, Sesto Fiorentino, FI, Italy
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