1
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Nepravishta R, Ramírez-Cárdenas J, Rocha G, Walpole S, Hicks T, Monaco S, Muñoz-García JC, Angulo J. Fast Quantitative Validation of 3D Models of Low-Affinity Protein-Ligand Complexes by STD NMR Spectroscopy. J Med Chem 2024; 67:10025-10034. [PMID: 38848103 PMCID: PMC11215723 DOI: 10.1021/acs.jmedchem.4c00204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/26/2024] [Accepted: 05/27/2024] [Indexed: 06/28/2024]
Abstract
Low-affinity protein-ligand interactions are important for many biological processes, including cell communication, signal transduction, and immune responses. Structural characterization of these complexes is also critical for the development of new drugs through fragment-based drug discovery (FBDD), but it is challenging due to the low affinity of fragments for the binding site. Saturation transfer difference (STD) NMR spectroscopy has revolutionized the study of low-affinity receptor-ligand interactions enabling binding detection and structural characterization. Comparison of relaxation and exchange matrix calculations with 1H STD NMR experimental data is essential for the validation of 3D structures of protein-ligand complexes. In this work, we present a new approach based on the calculation of a reduced relaxation matrix, in combination with funnel metadynamics MD simulations, that allows a very fast generation of experimentally STD-NMR-validated 3D structures of low-affinity protein-ligand complexes.
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Affiliation(s)
- Ridvan Nepravishta
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
- Cancer Research Horizons, CRUK Scotland Institute, Garscube Estate, Switchback Road, Bearsden, Glasgow G61 1BD, U.K
| | - Jonathan Ramírez-Cárdenas
- Institute for Chemical Research (IIQ), CSIC - University of Seville, 49 Américo Vespucio, 41092 Seville, Spain
| | - Gabriel Rocha
- Institute for Chemical Research (IIQ), CSIC - University of Seville, 49 Américo Vespucio, 41092 Seville, Spain
| | - Samuel Walpole
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
| | - Thomas Hicks
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
| | - Serena Monaco
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
| | - Juan C Muñoz-García
- Institute for Chemical Research (IIQ), CSIC - University of Seville, 49 Américo Vespucio, 41092 Seville, Spain
| | - Jesús Angulo
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
- Institute for Chemical Research (IIQ), CSIC - University of Seville, 49 Américo Vespucio, 41092 Seville, Spain
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2
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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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3
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Stampelou M, Ladds G, Kolocouris A. Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A 3 Receptor. J Phys Chem B 2024; 128:914-936. [PMID: 38236582 DOI: 10.1021/acs.jpcb.3c05986] [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: 01/19/2024]
Abstract
A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.
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Affiliation(s)
- Margarita Stampelou
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Graham Ladds
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, U.K
| | - Antonios Kolocouris
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771 Athens, Greece
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4
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Calderón JC, Plut E, Keller M, Cabrele C, Reiser O, Gervasio FL, Clark T. Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors. J Chem Inf Model 2024; 64:205-218. [PMID: 38150388 DOI: 10.1021/acs.jcim.3c01574] [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: 12/29/2023]
Abstract
A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.
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Affiliation(s)
- Jacqueline C Calderón
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
| | - Eva Plut
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Max Keller
- Institute of Pharmacy, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg D-93040, Germany
| | - Chiara Cabrele
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Oliver Reiser
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | | | - Timothy Clark
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
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5
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Arend C, Grothaus IL, Waespy M, Ciacchi LC, Dringen R. Modulation of Multidrug Resistance Protein 1-mediated Transport Processes by the Antiviral Drug Ritonavir in Cultured Primary Astrocytes. Neurochem Res 2024; 49:66-84. [PMID: 37603214 PMCID: PMC10776481 DOI: 10.1007/s11064-023-04008-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/25/2023] [Accepted: 08/01/2023] [Indexed: 08/22/2023]
Abstract
The Multidrug Resistance Protein 1 (Mrp1) is an ATP-dependent efflux transporter and a major facilitator of drug resistance in mammalian cells during cancer and HIV therapy. In brain, Mrp1-mediated GSH export from astrocytes is the first step in the supply of GSH precursors to neurons. To reveal potential mechanisms underlying the drug-induced modulation of Mrp1-mediated transport processes, we investigated the effects of the antiviral drug ritonavir on cultured rat primary astrocytes. Ritonavir strongly stimulated the Mrp1-mediated export of glutathione (GSH) by decreasing the Km value from 200 nmol/mg to 28 nmol/mg. In contrast, ritonavir decreased the export of the other Mrp1 substrates glutathione disulfide (GSSG) and bimane-glutathione. To give explanation for these apparently contradictory observations, we performed in silico docking analysis and molecular dynamics simulations using a homology model of rat Mrp1 to predict the binding modes of ritonavir, GSH and GSSG to Mrp1. The results suggest that ritonavir binds to the hydrophilic part of the bipartite binding site of Mrp1 and thereby differently affects the binding and transport of the Mrp1 substrates. These new insights into the modulation of Mrp1-mediated export processes by ritonavir provide a new model to better understand GSH-dependent detoxification processes in brain cells.
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Affiliation(s)
- Christian Arend
- Centre for Biomolecular Interactions Bremen, Faculty 2 (Biology/Chemistry), University of Bremen, P.O. Box 330440, 28359, Bremen, Germany.
- Centre for Environmental Research and Sustainable Technology, University of Bremen, Bremen, Germany.
| | - Isabell L Grothaus
- Centre for Environmental Research and Sustainable Technology, University of Bremen, Bremen, Germany
- Hybrid Materials Interfaces Group, Faculty of Production Engineering, Bremen Center for Computational Materials Science, MAPEX Center for Materials and Processes, University of Bremen, Am Fallturm 1, 28359, Bremen, Germany
| | - Mario Waespy
- Centre for Biomolecular Interactions Bremen, Faculty 2 (Biology/Chemistry), University of Bremen, P.O. Box 330440, 28359, Bremen, Germany
| | - Lucio Colombi Ciacchi
- Centre for Environmental Research and Sustainable Technology, University of Bremen, Bremen, Germany
- Hybrid Materials Interfaces Group, Faculty of Production Engineering, Bremen Center for Computational Materials Science, MAPEX Center for Materials and Processes, University of Bremen, Am Fallturm 1, 28359, Bremen, Germany
| | - Ralf Dringen
- Centre for Biomolecular Interactions Bremen, Faculty 2 (Biology/Chemistry), University of Bremen, P.O. Box 330440, 28359, Bremen, Germany
- Centre for Environmental Research and Sustainable Technology, University of Bremen, Bremen, Germany
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6
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Rubina, Moin ST. Attempting Well-Tempered Funnel Metadynamics Simulations for the Evaluation of the Binding Kinetics of Methionine Aminopeptidase-II Inhibitors. J Chem Inf Model 2023; 63:7729-7743. [PMID: 38059911 DOI: 10.1021/acs.jcim.3c01130] [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: 12/08/2023]
Abstract
Understanding the unbinding kinetics of protein-ligand complexes is considered a significant approach for the design of ligands with desired specificity and safety. In recent years, enhanced sampling methods have emerged as effective tools for studying the unbinding kinetics of protein-ligand complexes at the atomistic level. MetAP-II is a target for the treatment of cancer for which not a single effective drug is available yet. The identification of the dissociation rate of ligands from the complexes often serves as a better predictor for in vivo efficacy than the ligands' binding affinity. Here, funnel-based restraint well-tempered metadynamics simulations were applied to predict the residence time of two ligands bound to MetAP-II, along with the ligand association and dissociation mechanism involving the identification of the binding hotspot during ligand egress. The ligand-egressing route revealed by metadynamics simulations also correlated with the identified pathways from the CAVER analysis and by the enhanced sampling simulation using PLUMED. Ligand 1 formed a strong H-bond interaction with GLU364 estimating a higher residence time of 28.22 ± 5.29 ns in contrast to ligand 2 with a residence time of 19.05 ± 3.58 ns, which easily dissociated from the binding pocket of MetAP-II. The results obtained from the simulations were consistent to reveal ligand 1 being superior to ligand 2; however, the experimental data related to residence time were close for both ligands, and no kinetic data were available for ligand 2. The current study could be considered the first attempt to apply an enhanced sampling method for the evaluation of the binding kinetics and thermodynamics of two different classes of ligands to a binuclear metalloprotein.
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Affiliation(s)
- Rubina
- Third World Center for Science and Technology H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Science University of Karachi, Karachi 75270, Pakistan
| | - Syed Tarique Moin
- Third World Center for Science and Technology H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Science University of Karachi, Karachi 75270, Pakistan
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7
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Fu H, Chipot C, Shao X, Cai W. Standard Binding Free-Energy Calculations: How Far Are We from Automation? J Phys Chem B 2023; 127:10459-10468. [PMID: 37824848 DOI: 10.1021/acs.jpcb.3c04370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.
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Affiliation(s)
- Haohao Fu
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Christophe Chipot
- Laboratoire International Associé CNRS and University of Illinois at Urbana-Champaign, UMR no. 7019, Université de Lorraine, BP 70239, F-54506 Vandoeuvre-lès-Nancy, France
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois 61801, United States
- Department of Chemistry, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
- Department of Chemistry, The University of Hawai'i at Ma̅noa, 2545 McCarthy Mall, Honolulu, Hawaii 96822, United States
| | - Xueguang Shao
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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8
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Bobrovs R, Drunka L, Kanepe I, Jirgensons A, Caflisch A, Salvalaglio M, Jaudzems K. Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors. J Chem Inf Model 2023; 63:6890-6899. [PMID: 37801405 DOI: 10.1021/acs.jcim.3c00826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.
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Affiliation(s)
- Raitis Bobrovs
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Laura Drunka
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Iveta Kanepe
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Aigars Jirgensons
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Matteo Salvalaglio
- Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom
| | - Kristaps Jaudzems
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
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9
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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 PMCID: PMC11219094 DOI: 10.1021/acs.jctc.3c00333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [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, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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10
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Motta S, Siani P, Donadoni E, Frigerio G, Bonati L, Di Valentin C. Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles. NANOSCALE 2023; 15:7909-7919. [PMID: 37066796 DOI: 10.1039/d3nr00397c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Inorganic nanoparticles show promising properties that allow them to be efficiently used as drug carriers. The main limitation in this type of application is currently the drug loading capacity, which can be overcome with a proper functionalization of the nanoparticle surface. In this study, we present, for the first time, a computational approach based on metadynamics to estimate the binding free energy of the doxorubicin drug (DOX) to a functionalized TiO2 nanoparticle under different pH conditions. On a thermodynamic basis, we demonstrate the robustness of our approach to capture the overall mechanism behind the pH-triggered release of DOX due to environmental pH changes. Notably, binding free energy estimations align well with what is expected for a pH-sensitive drug delivery system. Based on our results, we envision the use of metadynamics as a promising computational tool for the rational design and in silico optimization of organic ligands with improved drug carrier properties.
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Affiliation(s)
- Stefano Motta
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Paulo Siani
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Edoardo Donadoni
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Giulia Frigerio
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Laura Bonati
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Cristiana Di Valentin
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
- BioNanoMedicine Center NANOMIB, University of Milano-Bicocca, Italy
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11
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Nde J, Zhang P, Waxham MN, Cheung MS. Experiment and Simulation Reveal Residue Details for How Target Binding Tunes Calmodulin's Calcium-Binding Properties. J Phys Chem B 2023; 127:2900-2908. [PMID: 36977372 DOI: 10.1021/acs.jpcb.2c08734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
We aim to elucidate the molecular mechanism of the reciprocal relation of calmodulin's (CaM) target binding and its affinity for calcium ions (Ca2+), which is central to decoding CaM-dependent Ca2+ signaling in a cell. We employed stopped-flow experiments and coarse-grained molecular simulations that learn the coordination chemistry of Ca2+ in CaM from first-principle calculations. The associative memories as part of the coarse-grained force fields built on known protein structures further influence CaM's selection of its polymorphic target peptides in the simulations. We modeled the peptides from the Ca2+/CaM-binding domain of Ca2+/CaM-dependent kinase II (CaMKII), CaMKIIp (293-310) and selected distinctive mutations at the N-terminus. Our stopped-flow experiments have shown that the CaM's affinity for Ca2+ in the bound complex of Ca2+/CaM/CaMKIIp decreased significantly when Ca2+/CaM bound to the mutant peptide (296-AAA-298) compared to that bound to the wild-type peptide (296-RRK-298). The coarse-grained molecular simulations revealed that the 296-AAA-298 mutant peptide destabilized the structures of Ca2+-binding loops at the C-domain of CaM (c-CaM) due to both loss of electrostatic interactions and differences in polymorphic structures. We have leveraged a powerful coarse-grained approach to advance a residue-level understanding of the reciprocal relation in CaM, that could not be possibly achieved by other computational approaches.
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Affiliation(s)
- Jules Nde
- Department of Physics, University of Washington, Seattle, Washington 98105, United States
| | - Pengzhi Zhang
- Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, Texas 77030, United States
| | - M Neal Waxham
- Department of Neurobiology and Anatomy, University of Texas Health Science Center, Houston, Texas 77030, United States
| | - Margaret S Cheung
- Department of Physics, University of Washington, Seattle, Washington 98105, United States
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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12
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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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13
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Lukauskis D, Samways ML, Aureli S, Cossins BP, Taylor RD, Gervasio FL. Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses. J Chem Inf Model 2022; 62:6209-6216. [PMID: 36401553 DOI: 10.1021/acs.jcim.2c01142] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.
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Affiliation(s)
- Dominykas Lukauskis
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom
| | | | - Simone Aureli
- Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland
| | - Benjamin P Cossins
- UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom.,Exscientia Ltd., The Schrödinger Building, Oxford Science Park, OxfordOX4 4GE, United Kingdom
| | | | - Francesco Luigi Gervasio
- Department of Chemistry, University College London, LondonWC1E 6BT, United Kingdom.,Biomolecular and Pharmaceutical Modelling Group, School of Pharmaceutical Sciences, University of Geneva, CH1211Geneva, Switzerland.,Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, CH1211Geneva, Switzerland.,UCB, 216 Bath Road, SloughSL1 3WE, United Kingdom
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14
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Fu H, Zhou Y, Jing X, Shao X, Cai W. Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy. J Med Chem 2022; 65:12970-12978. [PMID: 36179112 DOI: 10.1021/acs.jmedchem.2c00796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.
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Affiliation(s)
- Haohao Fu
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
| | - Yan Zhou
- School of Medicine, Nankai University, Tianjin300071, China.,Department of Ultrasound, Tianjin Third Central Hospital, Tianjin300170, China
| | - Xiang Jing
- Department of Ultrasound, Tianjin Third Central Hospital, Tianjin300170, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
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15
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Ketkaew R, Creazzo F, Luber S. Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space. J Phys Chem Lett 2022; 13:1797-1805. [PMID: 35171614 DOI: 10.1021/acs.jpclett.1c04004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of a priori knowledge, enabling one to automatically search for hidden CVs of the reaction of interest.
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Affiliation(s)
- Rangsiman Ketkaew
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Fabrizio Creazzo
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Sandra Luber
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
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16
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Badaoui M, Buigues PJ, Berta D, Mandana GM, Gu H, Földes T, Dickson CJ, Hornak V, Kato M, Molteni C, Parsons S, Rosta E. Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics. J Chem Theory Comput 2022; 18:2543-2555. [PMID: 35195418 PMCID: PMC9097281 DOI: 10.1021/acs.jctc.1c00924] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
![]()
The
determination of drug residence times, which define the time
an inhibitor is in complex with its target, is a fundamental part
of the drug discovery process. Synthesis and experimental measurements
of kinetic rate constants are, however, expensive and time consuming.
In this work, we aimed to obtain drug residence times computationally.
Furthermore, we propose a novel algorithm to identify molecular design
objectives based on ligand unbinding kinetics. We designed an enhanced
sampling technique to accurately predict the free-energy profiles
of the ligand unbinding process, focusing on the free-energy barrier
for unbinding. Our method first identifies unbinding paths determining
a corresponding set of internal coordinates (ICs) that form contacts
between the protein and the ligand; it then iteratively updates these
interactions during a series of biased molecular dynamics (MD) simulations
to reveal the ICs that are important for the whole of the unbinding
process. Subsequently, we performed finite-temperature string simulations
to obtain the free-energy barrier for unbinding using the set of ICs
as a complex reaction coordinate. Importantly, we also aimed to enable
the further design of drugs focusing on improved residence times.
To this end, we developed a supervised machine learning (ML) approach
with inputs from unbiased “downhill” trajectories initiated
near the transition state (TS) ensemble of the string unbinding path.
We demonstrate that our ML method can identify key ligand–protein
interactions driving the system through the TS. Some of the most important
drugs for cancer treatment are kinase inhibitors. One of these kinase
targets is cyclin-dependent kinase 2 (CDK2), an appealing target for
anticancer drug development. Here, we tested our method using two
different CDK2 inhibitors for the potential further development of
these compounds. We compared the free-energy barriers obtained from
our calculations with those observed in available experimental data.
We highlighted important interactions at the distal ends of the ligands
that can be targeted for improved residence times. Our method provides
a new tool to determine unbinding rates and to identify key structural
features of the inhibitors that can be used as starting points for
novel design strategies in drug discovery.
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Affiliation(s)
- Magd Badaoui
- Department of Chemistry, King's College London, London SE1 1DB, United Kingdom.,Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Pedro J Buigues
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Dénes Berta
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Gaurav M Mandana
- Department of Chemistry, King's College London, London SE1 1DB, United Kingdom
| | - Hankang Gu
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Tamás Földes
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Callum J Dickson
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Viktor Hornak
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Mitsunori Kato
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Carla Molteni
- Department of Physics, King's College London, London WC2R 2LS, United Kingdom
| | - Simon Parsons
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, United Kingdom
| | - Edina Rosta
- Department of Chemistry, King's College London, London SE1 1DB, United Kingdom.,Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
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17
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Zhang Q, Zhao N, Meng X, Yu F, Yao X, Liu H. The prediction of protein-ligand unbinding for modern drug discovery. Expert Opin Drug Discov 2021; 17:191-205. [PMID: 34731059 DOI: 10.1080/17460441.2022.2002298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods. AREAS COVERED In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed. EXPERT OPINION Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.
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Affiliation(s)
| | - Nannan Zhao
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Xiaoxiao Meng
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Fansen Yu
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, China.,Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - Huanxiang Liu
- School of Pharmacy, Lanzhou University, Lanzhou, China
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18
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Castelli M, Serapian SA, Marchetti F, Triveri A, Pirota V, Torielli L, Collina S, Doria F, Freccero M, Colombo G. New perspectives in cancer drug development: computational advances with an eye to design. RSC Med Chem 2021; 12:1491-1502. [PMID: 34671733 PMCID: PMC8459323 DOI: 10.1039/d1md00192b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 07/06/2021] [Indexed: 02/06/2023] Open
Abstract
Computational chemistry has come of age in drug discovery. Indeed, most pharmaceutical development programs rely on computer-based data and results at some point. Herein, we discuss recent applications of advanced simulation techniques to difficult challenges in drug discovery. These entail the characterization of allosteric mechanisms and the identification of allosteric sites or cryptic pockets determined by protein motions, which are not immediately evident in the experimental structure of the target; the study of ligand binding mechanisms and their kinetic profiles; and the evaluation of drug-target affinities. We analyze different approaches to tackle challenging and emerging biological targets. Finally, we discuss the possible perspectives of future application of computation in drug discovery.
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Affiliation(s)
- Matteo Castelli
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Stefano A Serapian
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Filippo Marchetti
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Alice Triveri
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Valentina Pirota
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Luca Torielli
- Department of Drug Sciences, Medicinal Chemistry and Pharmaceutical Technology Section, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Simona Collina
- Department of Drug Sciences, Medicinal Chemistry and Pharmaceutical Technology Section, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Filippo Doria
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Mauro Freccero
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
| | - Giorgio Colombo
- Department of Chemistry, University of Pavia via Taramelli 12 27100 Pavia Italy
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19
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Yasuda T, Morita R, Shigeta Y, Harada R. Independent Nontargeted Parallel Cascade Selection Molecular Dynamics (Ino-PaCS-MD) to Enhance the Conformational Sampling of Proteins. J Chem Theory Comput 2021; 17:5933-5943. [PMID: 34410106 DOI: 10.1021/acs.jctc.1c00558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Biological functions are related to long-time protein dynamics (rare events) that are induced over microseconds. Such protein dynamics can be investigated using molecular dynamics (MD) simulations. However, the detection of rare events remains challenging using conventional MD (cMD) since the accessible timescales of cMD are shorter than those of the biological functions. Recently, the parallel cascade selection MD (PaCS-MD) has been proposed to detect such rare events, wherein transition paths are generated between a given reactant and product. As an extension, the nontargeted PaCS-MD (nt-PaCS-MD) has been proposed to predict the transition paths without requiring reference to any product. Thus, as a further extension, we herein propose independent nt-PaCS-MD, namely, Ino-PaCS-MD, wherein multiple walkers are launched from a set of different starting configurations. Each walker repeats a cycle of restarting short-time MD simulations from configurations with high potentials for making transitions to neighboring metastable states. To further enhance the sampling ability, Ino-PaCS-MD temporarily stops the conformational search and periodically resets the starting configurations so that they are uniformly distributed in a conformational subspace, thereby preventing a given protein from being trapped in one of the metastable states. As a demonstration, Ino-PaCS-MD successfully detects rare events of a maltose-binding protein as open-close transitions with a nanosecond-order simulation time, although a microsecond-order cMD simulation failed to detect these rare events, showing the high sampling efficiency of Ino-PaCS-MD.
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Affiliation(s)
- Takunori Yasuda
- College of Biological Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-0821, Japan
| | - Rikuri Morita
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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20
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Chen H, Zhou R, Pang J, Guo Y, Chen J, Kang Y, Duan M, Hou T. Molecular View on the Dissociation Pathways and Transactivation Regulation Mechanism of Nonsteroidal GR Ligands. J Chem Inf Model 2021; 62:5233-5245. [PMID: 34506144 DOI: 10.1021/acs.jcim.1c00150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
As a major drug target for anti-inflammatory therapy, the glucocorticoid receptor (GR) regulates a wide range of physiological processes through transactivation (TA) or transrepression. GR TA is involved in many adverse effects of GR-targeting drugs, and therefore, the discovery of novel GR ligands with lower TA activity and longer residence time is quite urgent. Undoubtedly, understanding the ligand dissociation mechanisms and the structural basis of the TA regulation is crucial for the development of novel GR-targeting drugs. Here, we used random accelerated molecular dynamics (RAMD) and funnel metadynamics (FM) simulations to explore the dissociation mechanisms of 5 classic glucocorticoids and 6 nonsteroidal GR ligands. Multiple ligand dissociation pathways were discovered. The classic glucocorticoids exhibit a strong preference for Path I, and most nonsteroidal ligands tend to dissociate along mixed pathways. We also find that the distinct unbinding preferences for AZD2906 and AZD9567, two representative nonsteroidal ligands with similar scaffolds but different TA activities, are primarily determined by their different polar interactions with the surrounding residues. Notably, the binding of AZD9567 poses a substantial impact on the conformation of the GR homodimer interface, which provides a valuable clue to understand the mechanisms of the TA-related side effects induced by the adjustments of the homodimerization process. These findings are critical for the structure-based rational design of novel GR ligands with more potent anti-inflammatory potency and reduced side effects.
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Affiliation(s)
- Haiyi Chen
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China.,National Centre for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071 Hubei, China
| | - Rui Zhou
- National Centre for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071 Hubei, China
| | - Jinping Pang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Yue Guo
- National Centre for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071 Hubei, China
| | - Jiawen Chen
- National Centre for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071 Hubei, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Mojie Duan
- National Centre for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071 Hubei, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang, China
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21
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Bertazzo M, Gobbo D, Decherchi S, Cavalli A. Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy. J Chem Theory Comput 2021; 17:5287-5300. [PMID: 34260233 PMCID: PMC8389529 DOI: 10.1021/acs.jctc.1c00177] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Computational capabilities are rapidly increasing, primarily because of the availability of GPU-based architectures. This creates unprecedented simulative possibilities for the systematic and robust computation of thermodynamic observables, including the free energy of a drug binding to a target. In contrast to calculations of relative binding free energy, which are nowadays widely exploited for drug discovery, we here push the boundary of computing the binding free energy and the potential of mean force. We introduce a novel protocol that leverages enhanced sampling, machine learning, and ad hoc algorithms to limit human intervention, computing time, and free parameters in free energy calculations. We first validate the method on a host-guest system, and then we apply the protocol to glycogen synthase kinase 3 beta, a protein kinase of pharmacological interest. Overall, we obtain a good correlation with experimental values in relative and absolute terms. While we focus on protein-ligand binding, the strategy is of broad applicability to any complex event that can be described with a path collective variable. We systematically discuss key details that influence the final result. The parameters and simulation settings are available at PLUMED-NEST to allow full reproducibility.
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Affiliation(s)
- Martina Bertazzo
- Computational
& Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology (FaBiT), Alma
Mater Studiorum − University of Bologna, via Belmeloro 6, 40126 Bologna, Italy
| | - Dorothea Gobbo
- Computational
& Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy
| | - Sergio Decherchi
- Computational
& Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy
- BiKi
Technologies s.r.l., Via XX Settembre 33/10, 16121 Genoa, Italy
| | - Andrea Cavalli
- Computational
& Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology (FaBiT), Alma
Mater Studiorum − University of Bologna, via Belmeloro 6, 40126 Bologna, Italy
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22
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Codony S, Calvó-Tusell C, Valverde E, Osuna S, Morisseau C, Loza MI, Brea J, Pérez C, Rodríguez-Franco MI, Pizarro-Delgado J, Corpas R, Griñán-Ferré C, Pallàs M, Sanfeliu C, Vázquez-Carrera M, Hammock BD, Feixas F, Vázquez S. From the Design to the In Vivo Evaluation of Benzohomoadamantane-Derived Soluble Epoxide Hydrolase Inhibitors for the Treatment of Acute Pancreatitis. J Med Chem 2021; 64:5429-5446. [PMID: 33945278 PMCID: PMC8634379 DOI: 10.1021/acs.jmedchem.0c01601] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
![]()
The
pharmacological inhibition of soluble epoxide hydrolase (sEH)
is efficient for the treatment of inflammatory and pain-related diseases.
Numerous potent sEH inhibitors (sEHIs) present adamantyl or phenyl
moieties, such as the clinical candidates AR9281 or EC5026. Herein,
in a new series of sEHIs, these hydrophobic moieties have been merged
in a benzohomoadamantane scaffold. Most of the new sEHIs have excellent
inhibitory activities against sEH. Molecular dynamics simulations
suggested that the addition of an aromatic ring into the adamantane
scaffold produced conformational rearrangements in the enzyme to stabilize
the aromatic ring of the benzohomoadamantane core. A screening cascade
permitted us to select a candidate for an in vivo efficacy study in a murine model of cerulein-induced acute pancreatitis.
The administration of 22 improved the health status of
the animals and reduced pancreatic damage, demonstrating that the
benzohomoadamantane unit is a promising scaffold for the design of
novel sEHIs.
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Affiliation(s)
- Sandra Codony
- Laboratori de Química Farmacèutica (Unitat Associada al CSIC), Departament de Farmacologia, Toxicologia i Química Terapèutica, Facultat de Farmàcia i Ciències de l'Alimentació, and Institute of Biomedicine (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, Barcelona 08028, Spain
| | - Carla Calvó-Tusell
- CompBioLab Group, Departament de Química and Institut de Química Computacional i Catàlisi (IQCC), Universitat de Girona, C/ Maria Aurèlia Capmany 69, Girona 17003, Spain
| | - Elena Valverde
- Laboratori de Química Farmacèutica (Unitat Associada al CSIC), Departament de Farmacologia, Toxicologia i Química Terapèutica, Facultat de Farmàcia i Ciències de l'Alimentació, and Institute of Biomedicine (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, Barcelona 08028, Spain
| | - Sílvia Osuna
- CompBioLab Group, Departament de Química and Institut de Química Computacional i Catàlisi (IQCC), Universitat de Girona, C/ Maria Aurèlia Capmany 69, Girona 17003, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
| | - Christophe Morisseau
- Department of Entomology and Nematology and Comprehensive Cancer Center, University of California Davis, One Shields Avenue, Davis 95616, California, United States
| | - M Isabel Loza
- Drug Screening Platform/Biofarma Research Group, CIMUS Research Center. Departamento de Farmacoloxía, Farmacia e Tecnoloxía Farmacéutica, University of Santiago de Compostela (USC), Santiago de Compostela 15782, Spain
| | - José Brea
- Drug Screening Platform/Biofarma Research Group, CIMUS Research Center. Departamento de Farmacoloxía, Farmacia e Tecnoloxía Farmacéutica, University of Santiago de Compostela (USC), Santiago de Compostela 15782, Spain
| | - Concepción Pérez
- Institute of Medicinal Chemistry, Spanish National Research Council (CSIC), C/Juan de la Cierva 3, Madrid 28006, Spain
| | - María Isabel Rodríguez-Franco
- Institute of Medicinal Chemistry, Spanish National Research Council (CSIC), C/Juan de la Cierva 3, Madrid 28006, Spain
| | - Javier Pizarro-Delgado
- Pharmacology Section. Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institute of Biomedicine of the University of Barcelona (IBUB), University of Barcelona, Av. Joan XXIII, 27-31, Barcelona 08028, Spain.,Spanish Biomedical Research Center in Diabetes and Associated Metabolic Diseases (CIBERDEM)-Instituto de Salud Carlos III, Madrid 28029, Spain.,Pediatric Research Institute-Hospital Sant Joan de Déu, Esplugues de Llobregat 08950, Spain
| | - Rubén Corpas
- Institute of Biomedical Research of Barcelona (IIBB), CSIC and IDIBAPS, Barcelona 08036, Spain.,CIBER Epidemiology and Public Health (CIBERESP)-Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Christian Griñán-Ferré
- Pharmacology Section. Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institut de Neurociències, University of Barcelona, Av. Joan XXIII, 27-31, Barcelona 08028, Spain
| | - Mercè Pallàs
- Pharmacology Section. Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institut de Neurociències, University of Barcelona, Av. Joan XXIII, 27-31, Barcelona 08028, Spain
| | - Coral Sanfeliu
- Institute of Biomedical Research of Barcelona (IIBB), CSIC and IDIBAPS, Barcelona 08036, Spain.,CIBER Epidemiology and Public Health (CIBERESP)-Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Manuel Vázquez-Carrera
- Pharmacology Section. Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institute of Biomedicine of the University of Barcelona (IBUB), University of Barcelona, Av. Joan XXIII, 27-31, Barcelona 08028, Spain.,Spanish Biomedical Research Center in Diabetes and Associated Metabolic Diseases (CIBERDEM)-Instituto de Salud Carlos III, Madrid 28029, Spain.,Pediatric Research Institute-Hospital Sant Joan de Déu, Esplugues de Llobregat 08950, Spain
| | - Bruce D Hammock
- Department of Entomology and Nematology and Comprehensive Cancer Center, University of California Davis, One Shields Avenue, Davis 95616, California, United States
| | - Ferran Feixas
- CompBioLab Group, Departament de Química and Institut de Química Computacional i Catàlisi (IQCC), Universitat de Girona, C/ Maria Aurèlia Capmany 69, Girona 17003, Spain
| | - Santiago Vázquez
- Laboratori de Química Farmacèutica (Unitat Associada al CSIC), Departament de Farmacologia, Toxicologia i Química Terapèutica, Facultat de Farmàcia i Ciències de l'Alimentació, and Institute of Biomedicine (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, Barcelona 08028, Spain
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23
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Rizzi V, Bonati L, Ansari N, Parrinello M. The role of water in host-guest interaction. Nat Commun 2021; 12:93. [PMID: 33397926 PMCID: PMC7782548 DOI: 10.1038/s41467-020-20310-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.
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Affiliation(s)
- Valerio Rizzi
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092, Zurich, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland
| | - Luigi Bonati
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland
- Department of Physics, ETH Zurich, 8092, Zurich, Switzerland
| | - Narjes Ansari
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092, Zurich, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092, Zurich, Switzerland.
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900, Lugano, Switzerland.
- Italian Institute of Technology, Via Morego 30, 16163, Genova, Italy.
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24
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Dutta P, Sengupta N. Expectation maximized molecular dynamics: Toward efficient learning of rarely sampled features in free energy surfaces from unbiased simulations. J Chem Phys 2020; 153:154104. [DOI: 10.1063/5.0021910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Pallab Dutta
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741246, India
| | - Neelanjana Sengupta
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741246, India
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