1
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Borowska AM, Chiariello MG, Garaeva AA, Rheinberger J, Marrink SJ, Paulino C, Slotboom DJ. Structural basis of the obligatory exchange mode of human neutral amino acid transporter ASCT2. Nat Commun 2024; 15:6570. [PMID: 39095408 PMCID: PMC11297037 DOI: 10.1038/s41467-024-50888-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
ASCT2 is an obligate exchanger of neutral amino acids, contributing to cellular amino acid homeostasis. ASCT2 belongs to the same family (SLC1) as Excitatory Amino Acid Transporters (EAATs) that concentrate glutamate in the cytosol. The mechanism that makes ASCT2 an exchanger rather than a concentrator remains enigmatic. Here, we employ cryo-electron microscopy and molecular dynamics simulations to elucidate the structural basis of the exchange mechanism of ASCT2. We establish that ASCT2 binds three Na+ ions per transported substrate and visits a state that likely acts as checkpoint in preventing Na+ ion leakage, both features shared with EAATs. However, in contrast to EAATs, ASCT2 retains one Na+ ion even under Na+-depleted conditions. We demonstrate that ASCT2 cannot undergo the structural transition in TM7 that is essential for the concentrative transport cycle of EAATs. This structural rigidity and the high-affinity Na+ binding site effectively confine ASCT2 to an exchange mode.
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Affiliation(s)
- Anna M Borowska
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands
| | - Maria Gabriella Chiariello
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology Institute, Molecular Dynamics Group, University of Groningen, Groningen, the Netherlands
| | - Alisa A Garaeva
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
| | - Jan Rheinberger
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands
- Biochemistry Center Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Siewert J Marrink
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology Institute, Molecular Dynamics Group, University of Groningen, Groningen, the Netherlands
| | - Cristina Paulino
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands.
- Biochemistry Center Heidelberg, Heidelberg University, Heidelberg, Germany.
| | - Dirk J Slotboom
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands.
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2
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Oh M, Rosa M, Xie H, Khelashvili G. Automated collective variable discovery for MFSD2A transporter from molecular dynamics simulations. Biophys J 2024:S0006-3495(24)00431-4. [PMID: 38932456 DOI: 10.1016/j.bpj.2024.06.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/03/2024] [Accepted: 06/24/2024] [Indexed: 06/28/2024] Open
Abstract
Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine-learning algorithms, such as principal component analysis (PCA), support vector machine, and linear discriminant analysis (LDA) based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.
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Affiliation(s)
- Myongin Oh
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Margarida Rosa
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Hengyi Xie
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - George Khelashvili
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.
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3
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Zhang M, Chen T, Lu X, Lan X, Chen Z, Lu S. G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery. Signal Transduct Target Ther 2024; 9:88. [PMID: 38594257 PMCID: PMC11004190 DOI: 10.1038/s41392-024-01803-6] [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: 08/15/2023] [Revised: 02/19/2024] [Accepted: 03/13/2024] [Indexed: 04/11/2024] Open
Abstract
G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.
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Affiliation(s)
- Mingyang Zhang
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ting Chen
- Department of Cardiology, Changzheng Hospital, Affiliated to Naval Medical University, Shanghai, 200003, China
| | - Xun Lu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaobing Lan
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Ziqiang Chen
- Department of Orthopedics, Changhai Hospital, Affiliated to Naval Medical University, Shanghai, 200433, China.
| | - Shaoyong Lu
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China.
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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4
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Beck TL, Carloni P, Asthagiri DN. All-Atom Biomolecular Simulation in the Exascale Era. J Chem Theory Comput 2024; 20:1777-1782. [PMID: 38382017 DOI: 10.1021/acs.jctc.3c01276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.
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Affiliation(s)
- Thomas L Beck
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Paolo Carloni
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
- Department of Physics, RWTH Aachen University, D-52078 Aachen, Germany
| | - Dilipkumar N Asthagiri
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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5
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Macke AC, Stump JE, Kelly MS, Rowley J, Herath V, Mullen S, Dima RI. Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms. J Chem Inf Model 2024; 64:470-482. [PMID: 38173388 DOI: 10.1021/acs.jcim.3c01511] [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: 01/05/2024]
Abstract
The identification and characterization of the main conformations from a protein population are a challenging and inherently high-dimensional problem. Here, we evaluate the performance of the Secondary sTructural Ensembles with machine LeArning (StELa) double-clustering method, which clusters protein structures based on the relationship between the φ and ψ dihedral angles in a protein backbone and the secondary structure of the protein, thus focusing on the local properties of protein structures. The classification of states as vectors composed of the clusters' indices arising naturally from the Ramachandran plot is followed by the hierarchical clustering of the vectors to allow for the identification of the main features of the corresponding free energy landscape (FEL). We compare the performance of StELa with the established root-mean-squared-deviation (RMSD)-based clustering algorithm, which focuses on global properties of protein structures and with Combinatorial Averaged Transient Structure (CATS), the combinatorial averaged transient structure clustering method based on distributions of the φ and ψ dihedral angle coordinates. Using ensembles of conformations from molecular dynamics simulations of intrinsically disordered proteins (IDPs) of various lengths (tau protein fragments) or short fragments from a globular protein, we show that StELa is the clustering method that identifies many of the minima and relevant energy states around the minima from the corresponding FELs. In contrast, the RMSD-based algorithm yields a large number of clusters that usually cover most of the FEL, thus being unable to distinguish between states, while CATS does not sample well the FELs for long IDPs and fragments from globular proteins.
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Affiliation(s)
- Amanda C Macke
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221, United States
| | - Jacob E Stump
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221, United States
| | - Maria S Kelly
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221, United States
| | - Jamie Rowley
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221, United States
| | - Vageesha Herath
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221, United States
- Department of Chemistry, Emory University, Atlanta, Georgia 30322, United States
| | - Sarah Mullen
- Department of Chemistry, The College of Wooster, Wooster, Ohio 44691, United States
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Ruxandra I Dima
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221, United States
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6
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Guo Y, Guo Y, Guo Z, Liu B, Xu J. Effect of Fragment 1 on the Binding of Epigallocatechin Gallate to the PD-L1 Dimer Explored by Molecular Dynamics. Molecules 2023; 28:7881. [PMID: 38067610 PMCID: PMC10708077 DOI: 10.3390/molecules28237881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
Blocking the interaction between programmed cell death-1 (PD-1) and programmed cell death-ligand 1 (PD-L1) by directly targeting the PD-L1 dimer has emerged as a hot topic in the field of cancer immunotherapy. Epigallocatechin gallate (EGCG), a natural product, has been demonstrated binding to the PD-L1 dimer in our previous study, but has a weaker binding capacity, moreover, EGCG is located at the end of the binding pocket of the PD-L1 dimer. The inhibitor fragment 1 (FRA) lies at the other end. So, we proposed that the introduction of FRA might be able to improve the binding ability. To illuminate this issue, molecular dynamics (MD) simulation was performed in the present study. Binding free energy calculations show that the binding affinity is significantly increased by 17 kcal/mol upon the introduction of FRA. It may be due to the energy contributions of emerging key residues ATyr56, AMet115, BTyr123, AIle54 and the enhanced contributions of initial key residues ATyr123 and BVal68. Binding mode and non-bonded interaction results indicate that FRA_EGCG (EGCG in combination with FRA) binds to the C-, F- and G-sheet of the PD-L1 dimer. Importantly, the introduction of FRA mainly strengthened the nonpolar interactions. The free energy landscape and secondary structure results further show that FRA_EGCG can interact with the PD-L1 dimer more stably. These data demonstrated here provide the theoretical basis for screening two or more natural products with additive inhibitory effect on this pathway and therefore exerting more effective anticancer immunity.
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Affiliation(s)
- Yan Guo
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (Y.G.); (Y.G.); (Z.G.)
| | - Yilin Guo
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (Y.G.); (Y.G.); (Z.G.)
| | - Zichao Guo
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (Y.G.); (Y.G.); (Z.G.)
| | - Boping Liu
- Key Laboratory for Bio-Based Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou 510630, China
| | - Jianguo Xu
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (Y.G.); (Y.G.); (Z.G.)
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7
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Buigues P, Gehrke S, Badaoui M, Dudas B, Mandana G, Qi T, Bottegoni G, Rosta E. Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor. J Chem Theory Comput 2023; 19:5260-5272. [PMID: 37458730 PMCID: PMC10413856 DOI: 10.1021/acs.jctc.3c00023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Indexed: 08/09/2023]
Abstract
Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3's activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization.
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Affiliation(s)
- Pedro
J. Buigues
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Sascha Gehrke
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Magd Badaoui
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Balint Dudas
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Gaurav Mandana
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Tianyun Qi
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Giovanni Bottegoni
- Dipartimento
di Scienze Biomolecolari (DISB), University
of Urbino, Urbino Piazza Rinascimento, 6, Urbino 61029, Italy
- Institute
of Clinical Sciences, University of Birmingham, Edgbaston, B15 2TT Birmingham, United Kingdom
| | - Edina Rosta
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
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8
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Bandyopadhyay S, Mondal J. A deep encoder-decoder framework for identifying distinct ligand binding pathways. J Chem Phys 2023; 158:2890463. [PMID: 37184003 DOI: 10.1063/5.0145197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The pathway(s) that a ligand would adopt en route to its trajectory to the native pocket of the receptor protein act as a key determinant of its biological activity. While Molecular Dynamics (MD) simulations have emerged as the method of choice for modeling protein-ligand binding events, the high dimensional nature of the MD-derived trajectories often remains a barrier in the statistical elucidation of distinct ligand binding pathways due to the stochasticity inherent in the ligand's fluctuation in the solution and around the receptor. Here, we demonstrate that an autoencoder based deep neural network, trained using an objective input feature of a large matrix of residue-ligand distances, can efficiently produce an optimal low-dimensional latent space that stores necessary information on the ligand-binding event. In particular, for a system of L99A mutant of T4 lysozyme interacting with its native ligand, benzene, this deep encoder-decoder framework automatically identifies multiple distinct recognition pathways, without requiring user intervention. The intermediates involve the spatially discrete location of the ligand in different helices of the protein before its eventual recognition of native pose. The compressed subspace derived from the autoencoder provides a quantitatively accurate measure of the free energy and kinetics of ligand binding to the native pocket. The investigation also recommends that while a linear dimensional reduction technique, such as time-structured independent component analysis, can do a decent job of state-space decomposition in cases where the intermediates are long-lived, autoencoder is the method of choice in systems where transient, low-populated intermediates can lead to multiple ligand-binding pathways.
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Affiliation(s)
- Satyabrata Bandyopadhyay
- Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences, Hyderabad 500046, India
| | - Jagannath Mondal
- Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences, Hyderabad 500046, India
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9
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Galvani F, Pala D, Cuzzolin A, Scalvini L, Lodola A, Mor M, Rizzi A. Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations. J Chem Inf Model 2023; 63:2842-2856. [PMID: 37053454 PMCID: PMC10170513 DOI: 10.1021/acs.jcim.3c00042] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the tMETA-D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure-kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed.
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Affiliation(s)
- Francesca Galvani
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Daniele Pala
- Chemistry Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Alberto Cuzzolin
- Chemistry Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Laura Scalvini
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Alessio Lodola
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Marco Mor
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
- Microbiome Research Hub, University of Parma, Parco Area delle Scienze 11/A, I-43124 Parma, Italy
| | - Andrea Rizzi
- Chemistry Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
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10
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Wolf S, Post M, Stock G. Path separation of dissipation-corrected targeted molecular dynamics simulations of protein-ligand unbinding. J Chem Phys 2023; 158:124106. [PMID: 37003731 DOI: 10.1063/5.0138761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Protein-ligand (un)binding simulations are a recent focus of biased molecular dynamics simulations. Such binding and unbinding can occur via different pathways in and out of a binding site. Here, we present a theoretical framework on how to compute kinetics along separate paths and on how to combine the path-specific rates into global binding and unbinding rates for comparison with experimental results. Using dissipation-corrected targeted molecular dynamics in combination with temperature-boosted Langevin equation simulations [S. Wolf et al., Nat. Commun. 11, 2918 (2020)] applied to a two-dimensional model and the trypsin-benzamidine complex as test systems, we assess the robustness of the procedure and discuss the aspects of its practical applicability to predict multisecond kinetics of complex biomolecular systems.
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Affiliation(s)
- Steffen Wolf
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany
| | - Matthias Post
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany
| | - Gerhard Stock
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany
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11
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Zuo K, Capelli R, Rossetti G, Nechushtai R, Carloni P. Predictions of the Poses and Affinity of a Ligand over the Entire Surface of a NEET Protein: The Case of Human MitoNEET. J Chem Inf Model 2023; 63:643-654. [PMID: 36623826 PMCID: PMC9875805 DOI: 10.1021/acs.jcim.2c01280] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Indexed: 01/11/2023]
Abstract
Human NEET proteins contain two [2Fe-2S] iron-sulfur clusters, bound to three Cys residues and one His residue. They exist in two redox states. Recently, these proteins have revealed themselves as attractive drug targets for mitochondrial dysfunction-related diseases, such as type 2 diabetes, Wolfram syndrome 2, and cancers. Unfortunately, the lack of information and mechanistic understanding of ligands binding to the whole functional, cytoplasmatic domain has limited rational drug design approaches. Here, we use an enhanced sampling technique, volume-based metadynamics, recently developed by a team involving some of us, to predict the poses and affinity of the 2-benzamido-4-(1,2,3,4-tetrahydronaphthalen-2-yl)-thiophene-3-carboxylate ligand to the entire surface of the cytoplasmatic domain of the human NEET protein mitoNEET (mNT) in an aqueous solution. The calculations, based on the recently published X-ray structure of the complex, are consistent with the measured affinity. The calculated free energy landscape revealed that the ligand can bind in multiple sites and with poses other than the one found in the X-ray. This difference is likely to be caused by crystal packing effects that allow the ligand to interact with multiple adjacent NEET protein copies. Such extra contacts are of course absent in the solution; therefore, the X-ray pose is only transient in our calculations, where the binding free energy correlates with the number of contacts. We further evaluated how the reduction and protonation of the Fe-bound histidine, as well as temperature, can affect ligand binding. Both such modifications introduce the possibility for the ligand to bind in an area of the protein other than the one observed in the X-ray, with no or little impact on affinity. Overall, our study can provide insights on the molecular recognition mechanisms of ligand binding to mNT in different oxidative conditions, possibly helping rational drug design of NEET ligands.
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Affiliation(s)
- Ke Zuo
- The
Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, 91904 Jerusalem, Israel
- Department
of Physics, RWTH Aachen University, 52074 Aachen, Germany
- Computational
Biomedicine, Institute of Advanced Simulation IAS-5 and Institute
of Neuroscience and Medicine INM-9, Forschungszentrum
Jülich GmbH, 52425 Jülich, Germany
- Department
of Physics, Università di Ferrara, 44121 Ferrara, Italy
| | - Riccardo Capelli
- Department
of Biosciences, Università degli
Studi di Milano, Via
Celoria 26, 20133 Milan, Italy
| | - Giulia Rossetti
- Computational
Biomedicine, Institute of Advanced Simulation IAS-5 and Institute
of Neuroscience and Medicine INM-9, Forschungszentrum
Jülich GmbH, 52425 Jülich, Germany
- Jülich
Supercomputing Center (JSC), Forschungszentrum
Jülich GmbH, 52425 Jülich, Germany
- Department
of Neurology, Faculty of Medicine, RWTH
Aachen University, 52074 Aachen, Germany
| | - Rachel Nechushtai
- The
Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, 91904 Jerusalem, Israel
| | - Paolo Carloni
- Department
of Physics, RWTH Aachen University, 52074 Aachen, Germany
- Computational
Biomedicine, Institute of Advanced Simulation IAS-5 and Institute
of Neuroscience and Medicine INM-9, Forschungszentrum
Jülich GmbH, 52425 Jülich, Germany
- JARA
Institute: Molecular Neuroscience and Imaging, Institute of Neuroscience
and Medicine INM-11, Forschungszentrum Jülich
GmbH, 52425 Jülich, Germany
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12
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Ramis R, Ballesteros ÓR, Muguruza-Montero A, M-Alicante S, Núñez E, Villarroel Á, Leonardo A, Bergara A. Molecular dynamics simulations of the calmodulin-induced α-helix in the SK2 calcium-gated potassium ion channel. J Biol Chem 2022; 299:102850. [PMID: 36587765 PMCID: PMC9874072 DOI: 10.1016/j.jbc.2022.102850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 12/21/2022] [Accepted: 12/23/2022] [Indexed: 12/30/2022] Open
Abstract
The family of small-conductance Ca2+-activated potassium ion channels (SK channels) is composed of four members (SK1, SK2, SK3, and SK4) involved in neuron-firing regulation. The gating of these channels depends on the intracellular Ca2+ concentration, and their sensitivity to this ion is provided by calmodulin (CaM). This protein binds to a specific region in SK channels known as the calmodulin-binding domain (CaMBD), an event which is essential for their gating. While CaMBDs are typically disordered in the absence of CaM, the SK2 channel subtype displays a small prefolded α-helical region in its CaMBD even if CaM is not present. This small helix is known to turn into a full α-helix upon CaM binding, although the molecular-level details for this conversion are not fully understood yet. In this work, we offer new insights on this physiologically relevant process by means of enhanced sampling, atomistic Hamiltonian replica exchange molecular dynamics simulations, providing a more detailed understanding of CaM binding to this target. Our results show that CaM is necessary for inducing a full α-helix along the SK2 CaMBD through hydrophobic interactions with V426 and L427. However, it is also necessary that W431 does not compete for these interactions; the role of the small prefolded α-helix in the SK2 CaMBD would be to stabilize W431 so that this is the case. In conclusion, our findings provide further insight into a key interaction between CaM and SK channels that is important for channel sensitivity to Ca2+.
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Affiliation(s)
- Rafael Ramis
- Donostia International Physics Center, Donostia, Spain; Departamento de Física, Universidad del País Vasco, UPV/EHU, Leioa, Spain.
| | - Óscar R. Ballesteros
- Departamento de Física, Universidad del País Vasco, UPV/EHU, Leioa, Spain,Centro de Física de Materiales CFM, CSIC-UPV/EHU, Donostia, Spain
| | | | - Sara M-Alicante
- Departamento de Física, Universidad del País Vasco, UPV/EHU, Leioa, Spain,Instituto Biofisika, CSIC-UPV/EHU, Leioa, Spain
| | - Eider Núñez
- Departamento de Física, Universidad del País Vasco, UPV/EHU, Leioa, Spain,Instituto Biofisika, CSIC-UPV/EHU, Leioa, Spain
| | | | - Aritz Leonardo
- Donostia International Physics Center, Donostia, Spain,Departamento de Física, Universidad del País Vasco, UPV/EHU, Leioa, Spain
| | - Aitor Bergara
- Donostia International Physics Center, Donostia, Spain,Departamento de Física, Universidad del País Vasco, UPV/EHU, Leioa, Spain,Centro de Física de Materiales CFM, CSIC-UPV/EHU, Donostia, Spain
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13
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Nada H. Effect of nitrogen molecules on the growth kinetics at the interface between a (111) plane of cubic ice and water. J Chem Phys 2022; 157:124701. [DOI: 10.1063/5.0106842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The molecular-scale growth kinetics of ice from water in the presence of air molecules are still poorly understood, despite their importance for understanding ice particle formation in nature. In this study, a molecular dynamics simulation is conducted to elucidate the molecular-scale growth kinetics at the interface between a (111) plane of cubic ice and water in the presence of N2 molecules. Two potential models of N2 molecules with and without atomic charges are examined. For both models, N2 molecules bind stably to the interface for a period of 1 ns or longer, and the stability of the binding is higher for the charged model than for the noncharged model. Free-energy surfaces of an N2 molecule along the interface and along an ideal (111) plane surface of cubic ice suggest that for both models, the position where an N2 molecule binds stably is different at the interface and on the ideal plane surface, and the stability of the binding is much higher for the interface than for the ideal plane surface. For both models, stacking-disordered ice grows at the interface, and the formation probability of a hexagonal ice layer in the stacking-disordered ice is higher for the charged model than for the uncharged model. The formation probability for the hexagonal ice layer in the stacking-disordered ice depends not only on the stability of binding but also on the positions where N2 molecules bind on the underlying ice, and the number of N2 molecules that bind stably to the underlying ice.
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Affiliation(s)
- Hiroki Nada
- Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology, Japan
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14
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Aida H, Shigeta Y, Harada R. The role of ATP in solubilizing RNA-binding protein fused in sarcoma. Proteins 2022; 90:1606-1612. [PMID: 35297101 DOI: 10.1002/prot.26335] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/05/2022] [Accepted: 03/10/2022] [Indexed: 12/29/2022]
Abstract
Intrinsically disordered protein (IDP) plays an important role in liquid-liquid phase separation (LLPS). RNA-binding protein fused in sarcoma (FUS) is a well-studied IDP that induces LLPS since its low-complexity core region (FUS-LC-core) is essential for droplet formation through contacts between FUS-LC-cores. Several experimental studies have reported that adenosine triphosphate (ATP) concentrations modulate LLPS-driven droplet formation through the dissolution of FUS. To elucidate the role of ATP in this dissolution, microsecond-order all-atom molecular dynamics (MD) simulations were performed for a crowded system of FUS-LC-cores in the presence of multiple ATP molecules. Our analysis revealed that the adenine group of ATP frequently contacted the FUS-LC-core, and the phosphoric acid group of ATP was exposed to the external solvent, which promoted both hydration and solubilization of FUS.
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Affiliation(s)
- Hayato Aida
- College of Biological Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
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15
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Han X, Zhang J, Yang YI, Zhang Z, Yang L, Gao YQ. Enhanced Sampling Simulation Reveals How Solvent Influences Chirogenesis of the Intra-Molecular Diels-Alder Reaction. J Chem Theory Comput 2022; 18:4318-4326. [PMID: 35666128 DOI: 10.1021/acs.jctc.2c00233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The timescale involved in chemical reactions is quite often beyond that of normal molecular dynamics simulations. Here, we combine metadynamics with selective integrated tempering sampling to simulate an intra-molecular Diels-Alder reaction in explicit solvents. Based on a one-dimensional collective variable obtained from harmonic linear discriminant analysis, four chiral isomers of products were observed in the simulation. Analyses of reactive trajectories showed that this reaction follows a concerted mechanism in all four solvents. In addition, the hydrogen bond between the reactant and water solvent plays an important role in the water-accelerated reaction mechanism. The dynamics of chirality formation varies significantly with solvents. The chirality of products forms significantly before the transition state, especially in ionic liquid.
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Affiliation(s)
- Xu Han
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jun Zhang
- Changping Laboratory, Beijing 102206, China
| | - Yi Isaac Yang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China
| | - Zhen Zhang
- School of Physics and Technology, Tangshan Normal University, Tangshan 063000, China
| | - Lijiang Yang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China
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16
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Nada H. Stable Binding Conformations of Polymaleic and Polyacrylic Acids at a Calcite Surface in the Presence of Countercations: A Metadynamics Study. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2022; 38:7046-7057. [PMID: 35604639 DOI: 10.1021/acs.langmuir.2c00750] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Elucidating the stable binding conformations of additives at the surface of CaCO3 crystals is essential to biomineralization, scale inhibition, and materials technology. However, accomplishing this by experimental means is rather difficult. In this study, molecular dynamics simulations based on a metadynamics approach were conducted to elucidate the stable binding conformations of a deprotonated polymaleic acid (PMA) additive and two deprotonated poly(acrylic acid) (PAA) additives with different polymerization degrees in the presence of various countercations at a hydrated calcite (104) surface. The simulated free-energy surfaces suggested the existence of several slightly different stable binding conformations for each additive. The appearance of these distinct binding conformations is speculated to originate from different balances of interactions between the additive, the calcite surface, and the countercations. The binding conformations and binding stabilities at the calcite surface were affected by the countercations, with Ca2+ ions producing a more pronounced effect than Na+ ions. Furthermore, the simulation results suggested that the binding stability at the calcite surface was higher for the PMA additive than for the PAA additives, and the PAA additive with a polymerization degree of 10 displayed a binding stability that was similar to or lower than that of the PAA additive with a polymerization degree of 5. The present simulation method provides a new strategy for analyzing the binding conformations of complex additives at material surfaces, developing additives that stably bind to these surfaces, and designing additives to control crystal growth.
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Affiliation(s)
- Hiroki Nada
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8569, Japan
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17
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Wu X, Xu LY, Li EM, Dong G. Application of molecular dynamics simulation in biomedicine. Chem Biol Drug Des 2022; 99:789-800. [PMID: 35293126 DOI: 10.1111/cbdd.14038] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/25/2022] [Accepted: 03/05/2022] [Indexed: 02/05/2023]
Abstract
Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein-protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function.
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Affiliation(s)
- Xiaodong Wu
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Li-Yan Xu
- Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
- Cancer Research Center, Shantou University Medical College, Shantou, China
| | - En-Min Li
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
| | - Geng Dong
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Medical Informatics Research Center, Shantou University Medical College, Shantou, China
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18
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Blomgren F, Rodin A, Chrobak W, Pacut DW, Swenson J, Ermilova I. Two statins and cromolyn as possible drugs against the cytotoxicity of Aβ(31-35) and Aβ(25-35) peptides: a comparative study by advanced computer simulation methods. RSC Adv 2022; 12:13352-13366. [PMID: 35520132 PMCID: PMC9066867 DOI: 10.1039/d2ra01963a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 04/19/2022] [Indexed: 11/21/2022] Open
Abstract
In this work, possible effective mechanisms of cromolyn, atorvastatin and lovastatin on the cytotoxicity of Aβ(31-35) and Aβ(25-35) peptides were investigated by classical molecular dynamics and well-tempered metadynamics simulations. The results demonstrate that all the drugs affect the behavior of the peptides, such as their ability to aggregate, and alter their secondary structures and their affinity to a particular drug. Our findings from the computed properties suggest that the best drug candidate is lovastatin. This medicine inhibits peptide aggregation, adsorbs the peptides on the surface of the drug clusters, changes the secondary structure and binds to MET35, which has been seen as the reason for the toxicity of the studied peptide sequences. Moreover, lovastatin is the drug which previously has demonstrated the strongest ability to penetrate the blood-brain barrier and makes lovastatin the most promising medicine among the three investigated drugs. Atorvastatin is also seen as a potential candidate if its penetration through the blood-brain barrier could be improved. Otherwise, its properties are even better than the ones demonstrated by lovastatin. Cromolyn appears to be less interesting as an anti-aggregant from the computational data, in comparison to the two statins.
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Affiliation(s)
- Fredrik Blomgren
- Department of Physics, Chalmers University of Technology Fysikgränd 4 Göteborg 41258 Sweden +46-728487773
| | - Alexander Rodin
- Department of Physics, Chalmers University of Technology Fysikgränd 4 Göteborg 41258 Sweden +46-728487773
| | - Wojciech Chrobak
- Department of Physics, Chalmers University of Technology Fysikgränd 4 Göteborg 41258 Sweden +46-728487773
| | - Dawid Wojciech Pacut
- Department of Physics, Chalmers University of Technology Fysikgränd 4 Göteborg 41258 Sweden +46-728487773
| | - Jan Swenson
- Department of Physics, Chalmers University of Technology Fysikgränd 4 Göteborg 41258 Sweden +46-728487773
| | - Inna Ermilova
- Department of Physics, Chalmers University of Technology Fysikgränd 4 Göteborg 41258 Sweden +46-728487773
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19
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Nicoli A, Dunkel A, Giorgino T, de Graaf C, Di Pizio A. Classification Model for the Second Extracellular Loop of Class A GPCRs. J Chem Inf Model 2022; 62:511-522. [PMID: 35113559 DOI: 10.1021/acs.jcim.1c01056] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.
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Affiliation(s)
- Alessandro Nicoli
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Andreas Dunkel
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Toni Giorgino
- Biophysics Institute, National Research Council (CNR-IBF), 20133 Milan, Italy
| | - Chris de Graaf
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG, U.K
| | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
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20
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Bhakat S. Collective variable discovery in the age of machine learning: reality, hype and everything in between. RSC Adv 2022; 12:25010-25024. [PMID: 36199882 PMCID: PMC9437778 DOI: 10.1039/d2ra03660f] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/20/2022] [Indexed: 11/21/2022] Open
Abstract
Understanding the kinetics and thermodynamics profile of biomolecules is necessary to understand their functional roles which has a major impact in mechanism driven drug discovery. Molecular dynamics simulation has been routinely used to understand conformational dynamics and molecular recognition in biomolecules. Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulation requires identification of a few low-dimensional variables which can describe the essential dynamics of a system without significant loss of information. In physical chemistry, these low-dimensional variables are often called collective variables. Collective variables are used to generate reduced representations of free energy surfaces and calculate transition probabilities between different metastable basins. However the choice of collective variables is not trivial for complex systems. Collective variables range from geometric criteria such as distances and dihedral angles to abstract ones such as weighted linear combinations of multiple geometric variables. The advent of machine learning algorithms led to increasing use of abstract collective variables to represent biomolecular dynamics. In this review, I will highlight several nuances of commonly used collective variables ranging from geometric to abstract ones. Further, I will put forward some cases where machine learning based collective variables were used to describe simple systems which in principle could have been described by geometric ones. Finally, I will put forward my thoughts on artificial general intelligence and how it can be used to discover and predict collective variables from spatiotemporal data generated by molecular dynamics simulations. Data driven collective variable discovery methods to capture conformational dynamics in biological macromolecules.![]()
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Affiliation(s)
- Soumendranath Bhakat
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104-6059, USA
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21
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Peptide Dynamics and Metadynamics: Leveraging Enhanced Sampling Molecular Dynamics to Robustly Model Long-Timescale Transitions. Methods Mol Biol 2022; 2405:151-167. [PMID: 35298813 PMCID: PMC9313359 DOI: 10.1007/978-1-0716-1855-4_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Molecular dynamics simulations can in theory reveal the thermodynamics and kinetics of peptide conformational transitions at atomic-level resolution. However, even with modern computing power, they are limited in the timescales they can sample, which is especially problematic for peptides that are fully or partially disordered. Here, we discuss how the enhanced sampling methods accelerated molecular dynamics (aMD) and metadynamics can be leveraged in a complementary fashion to quickly explore conformational space and then robustly quantify the underlying free energy landscape. We apply these methods to two peptides that have an intrinsically disordered nature, the histone H3 and H4 N-terminal tails, and use metadynamics to compute the free energy landscape along collective variables discerned from aMD simulations. Results show that these peptides are largely disordered, with a slight preference for α-helical structures.
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22
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Zhao Q, Capelli R, Carloni P, Lüscher B, Li J, Rossetti G. Enhanced Sampling Approach to the Induced-Fit Docking Problem in Protein-Ligand Binding: The Case of Mono-ADP-Ribosylation Hydrolase Inhibitors. J Chem Theory Comput 2021; 17:7899-7911. [PMID: 34813698 DOI: 10.1021/acs.jctc.1c00649] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Enhanced sampling methods can predict free-energy landscapes associated with protein/ligand binding, characterizing the involved intermolecular interactions in a precise way. However, these in silico approaches can be challenged by induced-fit effects. Here, we present a variant of volume-based metadynamics tailored to tackle this problem in a general and efficient way. The validity of the approach is established by applying it to substrate/enzyme complexes of pharmacological relevance: mono-ADP-ribose (ADPr) in complex with mono-ADP-ribosylation hydrolases (MacroD1 and MacroD2), where induced-fit phenomena are known to be significant. The calculated binding free energies are consistent with experiments, with an absolute error smaller than 0.5 kcal/mol. Our simulations reveal that in all circumstances, the active loops, delimiting the boundaries of the binding site, undergo significant conformation rearrangements upon ligand binding. The calculations further provide, for the first time, the molecular basis of ADPr specificity and the relative changes in its experimental binding affinity on passing from MacroD1 to MacroD2 and all its mutants. Our study paves the way to the quantitative description of induced-fit events in molecular recognition.
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Affiliation(s)
- Qianqian Zhao
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,College of Chemistry, Fuzhou University, Fuzhou 350002, China
| | - Riccardo Capelli
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Department of Applied Science and Technology, Politecnico di Torino, Torino 10129, Italy
| | - Paolo Carloni
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Institute for Neuroscience and Medicine (INM)-11, Forschungszentrum Jülich, 52428 Jülich, Germany.,Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, 52062 Aachen, Germany.,Department of Neurology, RWTH Aachen University, 52062 Aachen, Germany
| | - Bernhard Lüscher
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, 52062 Aachen, Germany
| | - Jinyu Li
- College of Chemistry, Fuzhou University, Fuzhou 350002, China
| | - Giulia Rossetti
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Jülich Supercomputing Center (JSC), Forschungszentrum Jülich, 52428 Jülich, Germany.,Department of Neurology, RWTH Aachen University, 52062 Aachen, Germany
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23
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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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24
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Cecchini M, Changeux JP. Nicotinic receptors: From protein allostery to computational neuropharmacology. Mol Aspects Med 2021; 84:101044. [PMID: 34656371 DOI: 10.1016/j.mam.2021.101044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/28/2021] [Accepted: 09/30/2021] [Indexed: 11/15/2022]
Abstract
We propose an extension and further development of the Monod-Wyman-Changeux model for allosteric transitions of regulatory proteins to brain communications and specifically to neurotransmitters receptors, with the nicotinic acetylcholine receptor (nAChR) as a model of ligand-gated ion channels. The present development offers an expression of the change of the gating isomerization constant caused by pharmacological ligand binding in terms of its value in the absence of ligands and several "modulation factors", which vary with orthosteric ligand binding (agonists/antagonists), allosteric ligand binding (positive allosteric modulators/negative allosteric modulators) and receptor desensitization. The new - explicit - formulation of such "modulation factors", provides expressions for the pharmacological attributes of potency, efficacy, and selectivity for the modulatory ligands (including endogenous neurotransmitters) in terms of their binding affinity for the active, resting, and desensitized states of the receptor. The current formulation provides ways to design neuroactive compounds with a controlled pharmacological profile, opening the field of computational neuro-pharmacology.
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Affiliation(s)
- Marco Cecchini
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, F-67083, Strasbourg Cedex, France.
| | - Jean-Pierre Changeux
- Kavli Institute for Brain & Mind University of California, San Diego La Jolla, CA, 92093, USA; Institut Pasteur, URA 2182, CNRS, F-75015, France; Collège de France, F-75005 Paris, France.
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25
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Kokh DB, Wade RC. G Protein-Coupled Receptor-Ligand Dissociation Rates and Mechanisms from τRAMD Simulations. J Chem Theory Comput 2021; 17:6610-6623. [PMID: 34495672 DOI: 10.1021/acs.jctc.1c00641] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
There is a growing appreciation of the importance of drug-target binding kinetics for lead optimization. For G protein-coupled receptors (GPCRs), which mediate signaling over a wide range of time scales, the drug dissociation rate is often a better predictor of in vivo efficacy than binding affinity, although it is more challenging to compute. Here, we assess the ability of the τ-Random Acceleration Molecular Dynamics (τRAMD) approach to reproduce relative residence times and reveal dissociation mechanisms and the effects of allosteric modulation for two important membrane-embedded drug targets: the β2-adrenergic receptor and the muscarinic acetylcholine receptor M2. The dissociation mechanisms observed in the relatively short RAMD simulations (in which molecular dynamics (MD) simulations are performed using an additional force with an adaptively assigned random orientation applied to the ligand) are in general agreement with much more computationally intensive conventional MD and metadynamics simulations. Remarkably, although decreasing the magnitude of the random force generally reduces the number of egress routes observed, the ranking of ligands by dissociation rate is hardly affected and agrees well with experiment. The simulations also reproduce changes in residence time due to allosteric modulation and reveal associated changes in ligand dissociation pathways.
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Affiliation(s)
- Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
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26
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Bianciotto M, Gkeka P, Kokh DB, Wade RC, Minoux H. Contact Map Fingerprints of Protein-Ligand Unbinding Trajectories Reveal Mechanisms Determining Residence Times Computed from Scaled Molecular Dynamics. J Chem Theory Comput 2021; 17:6522-6535. [PMID: 34494849 DOI: 10.1021/acs.jctc.1c00453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-consuming experimental counterparts, even at an early drug discovery stage. Herein, we perform scaled molecular dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than seven chemical series to estimate their relative residence times. We introduce two new techniques for the analysis and the classification of the simulated unbinding trajectories. The first technique, which helps in estimating the limits of the free energy well around the bound state, and the second one, based on a new contact map fingerprint, allow the description and the comparison of the paths that lead to unbinding. Using these analyses, we find that ScaledMD's relative residence time generally enables the identification of the slowest unbinders. We propose an explanation for the underestimation of the residence times of a subset of compounds, and we investigate how the biasing in ScaledMD can affect the mechanistic insights that can be gained from the simulations.
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Affiliation(s)
- Marc Bianciotto
- Molecular Design Sciences, Sanofi R&D, 94403 Vitry-sur-Seine, France
| | - Paraskevi Gkeka
- Molecular Design Sciences, Sanofi R&D, 91 385 Chilly-Mazarin, France
| | - Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, 69120 Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Germany
| | - Hervé Minoux
- Data and Data Science, Sanofi R&D, 91 385 Chilly-Mazarin, France
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27
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Patmanidis I, Alessandri R, de Vries AH, Marrink SJ. Comparing Dimerization Free Energies and Binding Modes of Small Aromatic Molecules with Different Force Fields. Molecules 2021; 26:molecules26196069. [PMID: 34641613 PMCID: PMC8512883 DOI: 10.3390/molecules26196069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/25/2021] [Accepted: 09/26/2021] [Indexed: 11/16/2022] Open
Abstract
Dimerization free energies are fundamental quantities that describe the strength of interaction of different molecules. Obtaining accurate experimental values for small molecules and disentangling the conformations that contribute most to the binding can be extremely difficult, due to the size of the systems and the small energy differences. In many cases, one has to resort to computational methods to calculate such properties. In this work, we used molecular dynamics simulations in conjunction with metadynamics to calculate the free energy of dimerization of small aromatic rings, and compared three models from popular online servers for atomistic force fields, namely G54a7, CHARMM36 and OPLS. We show that, regardless of the force field, the profiles for the dimerization free energy of these compounds are very similar. However, significant care needs to be taken when studying larger molecules, since the deviations from the trends increase with the size of the molecules, resulting in force field dependent preferred stacking modes; for example, in the cases of pyrene and tetracene. Our results provide a useful background study for using topology builders to model systems which rely on stacking of aromatic moieties, and are relevant in areas ranging from drug design to supramolecular assembly.
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Affiliation(s)
- Ilias Patmanidis
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands; (I.P.); (R.A.); (A.H.d.V.)
| | - Riccardo Alessandri
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands; (I.P.); (R.A.); (A.H.d.V.)
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Alex H. de Vries
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands; (I.P.); (R.A.); (A.H.d.V.)
| | - Siewert J. Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands; (I.P.); (R.A.); (A.H.d.V.)
- Correspondence:
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28
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Chowdhury R, Sai Sreyas Adury V, Vijay A, Singh RK, Mukherjee A. Atomistic De-novo Inhibitor Generation-Guided Drug Repurposing for SARS-CoV-2 Spike Protein with Free-Energy Validation by Well-Tempered Metadynamics. Chem Asian J 2021; 16:1634-1642. [PMID: 33949124 PMCID: PMC8207131 DOI: 10.1002/asia.202100268] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/20/2021] [Indexed: 12/13/2022]
Abstract
Computational drug design is increasingly becoming important with new and unforeseen diseases like COVID-19. In this study, we present a new computational de novo drug design and repurposing method and applied it to find plausible drug candidates for the receptor binding domain (RBD) of SARS-CoV-2 (COVID-19). Our study comprises three steps: atom-by-atom generation of new molecules around a receptor, structural similarity mapping to existing approved and investigational drugs, and validation of their binding strengths to the viral spike proteins based on rigorous all-atom, explicit-water well-tempered metadynamics free energy calculations. By choosing the receptor binding domain of the viral spike protein, we showed that some of our new molecules and some of the repurposable drugs have stronger binding to RBD than hACE2. To validate our approach, we also calculated the free energy of hACE2 and RBD, and found it to be in an excellent agreement with experiments. These pool of drugs will allow strategic repurposing against COVID-19 for a particular prevailing conditions.
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Affiliation(s)
- Rituparno Chowdhury
- Department of ChemistryIndian Institute of Science Education and Research PuneMaharashtraIndia
- Department of Chemical SciencesIndian Institute of Science Education and Research KolkataWest BengalIndia
| | | | - Amal Vijay
- Department of ChemistryIndian Institute of Science Education and Research PuneMaharashtraIndia
| | - Reman K. Singh
- Department of ChemistryIndian Institute of Science Education and Research PuneMaharashtraIndia
| | - Arnab Mukherjee
- Department of ChemistryIndian Institute of Science Education and Research PuneMaharashtraIndia
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29
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Korshunova K, Carloni P. Ligand Affinities within the Open-Boundary Molecular Mechanics/Coarse-Grained Framework (I): Alchemical Transformations within the Hamiltonian Adaptive Resolution Scheme. J Phys Chem B 2021; 125:789-797. [PMID: 33443434 DOI: 10.1021/acs.jpcb.0c09805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Our recently developed Open-Boundary Molecular Mechanics/Coarse Grained (OB-MM/CG) framework predicts ligand poses in important pharmaceutical targets, such as G-protein Coupled Receptors, even when experimental structural information is lacking. The approach, which is based on GROMOS and AMBER force fields, allows for grand-canonical simulations of protein-ligand complexes by using the Hamiltonian Adaptive Resolution Scheme (H-AdResS) for the solvent. Here, we present a key step toward the estimation of ligand binding affinities for their targets within this approach. This is the implementation of the H-AdResS in the GROMACS code. The accuracy of our implementation is established by calculating hydration free energies of several molecules in water by means of alchemical transformations. The deviations of the GROMOS- and AMBER-based H-AdResS results from the reference fully atomistic simulations are smaller than the accuracy of the force field and/or they are in the range of the published results. Importantly, our predictions are in good agreement with experimental data. The current implementation paves the way to the use of the OB-MM/CG framework for the study of large biological systems.
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Affiliation(s)
- Ksenia Korshunova
- Department of Physics, RWTH Aachen University, 52074 Aachen, Germany.,Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Paolo Carloni
- Department of Physics, RWTH Aachen University, 52074 Aachen, Germany.,Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
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30
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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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31
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Decherchi S, Cavalli A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem Rev 2020; 120:12788-12833. [PMID: 33006893 PMCID: PMC8011912 DOI: 10.1021/acs.chemrev.0c00534] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Indexed: 12/19/2022]
Abstract
Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.
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Affiliation(s)
- Sergio Decherchi
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
| | - Andrea Cavalli
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology, University
of Bologna, 40126 Bologna, Italy
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32
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Kokh DB, Doser B, Richter S, Ormersbach F, Cheng X, Wade RC. A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories. J Chem Phys 2020; 153:125102. [DOI: 10.1063/5.0019088] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Affiliation(s)
- Daria B. Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Bernd Doser
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Stefan Richter
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Fabian Ormersbach
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Xingyi Cheng
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
- Molecular Biosciences, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
| | - Rebecca C. Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany
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33
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Capelli R, Lyu W, Bolnykh V, Meloni S, Olsen JMH, Rothlisberger U, Parrinello M, Carloni P. Accuracy of Molecular Simulation-Based Predictions of koff Values: A Metadynamics Study. J Phys Chem Lett 2020; 11:6373-6381. [PMID: 32672983 DOI: 10.1021/acs.jpclett.0c00999] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The koff values of ligands unbinding to proteins are key parameters for drug discovery. Their predictions based on molecular simulation may under- or overestimate experiment in a system- and/or technique-dependent way. Here we use an established method-infrequent metadynamics, based on the AMBER force field-to compute the koff of the ligand iperoxo (in clinical use) targeting the muscarinic receptor M2. The ligand charges are calculated by either (i) the Amber standard procedure or (ii) B3LYP-DFT. The calculations using (i) turn out not to provide a reasonable estimation of the transition-state free energy. Those using (ii) differ from experiment by 2 orders of magnitude. On the basis of B3LYP DFT QM/MM simulations, we suggest that the observed discrepancy in (ii) arises, at least in part, from the lack of electronic polarization and/or charge transfer in biomolecular force fields. These issues might be present in other systems, such as DNA-protein complexes.
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Affiliation(s)
- Riccardo Capelli
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- JARA-HPC, Forschungszentrum Jülich, D-54245 Jülich, Germany
| | - Wenping Lyu
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
| | - Viacheslav Bolnykh
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Simone Meloni
- Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Ferrara, Via Luigi Borsari 46, I-44121, Ferrara, Italy
| | - Jógvan Magnus Haugaard Olsen
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, UiT The Arctic University of Norway, N-9037 Tromsø, Norway
- Department of Chemistry, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences, ETH Zürich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland
- Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Paolo Carloni
- Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
- JARA-Institute INM-11: Molecular Neuroscience and Neuroimaging, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany
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34
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Fu H, Chen H, Wang X, Chai H, Shao X, Cai W, Chipot C. Finding an Optimal Pathway on a Multidimensional Free-Energy Landscape. J Chem Inf Model 2020; 60:5366-5374. [DOI: 10.1021/acs.jcim.0c00279] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Haohao Fu
- Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China
| | - Haochuan Chen
- Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China
| | - Xin’ao Wang
- Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China
| | - Hao Chai
- Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China
| | - Christophe Chipot
- Laboratoire International Associé CNRS and University of Illinois at Urbana−Champaign, F-54506 Vandœuvre-lès-Nancy, France
- Department of Physics, University of Illinois at Urbana−Champaign, 1110 West Green Street, Urbana, Illinois 61801, United States
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35
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Bonati L, Rizzi V, Parrinello M. Data-Driven Collective Variables for Enhanced Sampling. J Phys Chem Lett 2020; 11:2998-3004. [PMID: 32239945 DOI: 10.1021/acs.jpclett.0c00535] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the nonlinearly separable data set composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing nonlinear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.
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Affiliation(s)
- Luigi Bonati
- Department of Physics, ETH Zurich, 8092 Zurich, Switzerland
- Institute of Computational Sciences, Università della Svizzera italiana, via Buffi 13, 6900 Lugano, Switzerland
| | - Valerio Rizzi
- Institute of Computational Sciences, Università della Svizzera italiana, via Buffi 13, 6900 Lugano, Switzerland
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland
| | - Michele Parrinello
- Institute of Computational Sciences, Università della Svizzera italiana, via Buffi 13, 6900 Lugano, Switzerland
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland
- Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy
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36
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Horx P, Geyer A. Defining the mobility range of a hinge-type connection using molecular dynamics and metadynamics. PLoS One 2020; 15:e0230962. [PMID: 32282813 PMCID: PMC7153902 DOI: 10.1371/journal.pone.0230962] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 03/12/2020] [Indexed: 01/29/2023] Open
Abstract
A designed disulfide-rich β-hairpin peptide that dimerizes spontaneously served as a hinge-type connection between proteins. Here, we analyze the range of dynamics of this hinge dimer with the aim of proposing new applications for the DNA-encodable peptide and establishing guidelines for the computational analysis of other disulfide hinges. A recent structural analysis based on nuclear magnetic resonance spectroscopy and ion mobility spectrometry revealed an averaged conformation in the hinge region which motivated us to investigate the dynamic behavior using a combination of molecular dynamics simulation, metadynamics and free energy surface analysis to characterize the conformational space available to the hinge. Principal component analysis uncovered two slow modes of the peptide, namely, the opening and closing motion and twisting of the two β-hairpins assembling the hinge. Applying a collective variable (CV) that mimics the first dominating mode, led to a major expansion of the conformational space. The description of the dynamics could be achieved by analysis of the opening angle and the twisting of the β-hairpins and, thus, offers a methodology that can also be transferred to other derivatives. It has been demonstrated that the hinge peptide’s lowest energy conformation consists of a large opening angle and strong twist but is separated by small energy barriers and can, thus, adopt a closed and untwisted structure. With the aim of proposing further applications for the hinge peptide, we simulated its behavior in the sterically congested environment of a four-helix bundle. Preliminary investigations show that one helix is pushed out and a three-helix bundle forms. The insights gained into the dynamics of the tetra-disulfide peptide and analytical guidelines developed in this study may contribute to the understanding of the structure and function of more complex hinge-type proteins, such as the IgG antibody family.
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Affiliation(s)
- Philip Horx
- Department of Chemistry, Philipps-Universität Marburg, Marburg, Germany
| | - Armin Geyer
- Department of Chemistry, Philipps-Universität Marburg, Marburg, Germany
- * E-mail:
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37
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López-Serrano AL, De Jesús-Pérez JJ, Zamora-Cárdenas R, Ferrer T, Rodríguez-Menchaca AA, Tristani-Firouzi M, Moreno-Galindo EG, Navarro-Polanco RA. Voltage-induced structural modifications on M2 muscarinic receptor and their functional implications when interacting with the superagonist iperoxo. Biochem Pharmacol 2020; 177:113961. [PMID: 32272111 DOI: 10.1016/j.bcp.2020.113961] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 04/02/2020] [Indexed: 12/31/2022]
Abstract
It has been reported that muscarinic type-2 receptors (M2R) are voltage sensitive in an agonist-specific manner. In this work, we studied the effects of membrane potential on the interaction of M2R with the superagonist iperoxo (IXO), both functionally (using the activation of the ACh-gated K+ current (IKACh) in cardiomyocytes) and by molecular dynamics (MD) simulations. We found that IXO activated IKACh with remarkable high potency and clear voltage dependence, displaying a larger effect at the hyperpolarized potential. This result is consistent with a greater affinity, as validated by a slower (τ = 14.8 ± 2.3 s) deactivation kinetics of the IXO-evoked IKACh than that at the positive voltage (τ = 6.7 ± 1.2 s). The voltage-dependent M2R-IXO interaction induced IKACh to exhibit voltage-dependent features of this current, such as the 'relaxation gating' and the modulation of rectification. MD simulations revealed that membrane potential evoked specific conformational changes both at the external access and orthosteric site of M2R that underlie the agonist affinity change provoked by voltage on M2R. Moreover, our experimental data suggest that the 'tyrosine lid' (Y104, Y403, and Y426) is not the previously proposed voltage sensor of M2R. These findings provide an insight into the structural and functional framework of the biased signaling induced by voltage on GPCRs.
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Affiliation(s)
- Ana Laura López-Serrano
- Centro Universitario de Investigaciones Biomédicas, Universidad de Colima, Colima, Col., Mexico
| | - José J De Jesús-Pérez
- Instituto de Física, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, Mexico; Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Rodrigo Zamora-Cárdenas
- Centro Universitario de Investigaciones Biomédicas, Universidad de Colima, Colima, Col., Mexico
| | - Tania Ferrer
- Centro Universitario de Investigaciones Biomédicas, Universidad de Colima, Colima, Col., Mexico
| | - Aldo A Rodríguez-Menchaca
- Departamento de Fisiología y Biofísica, Facultad de Medicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, Mexico
| | - Martin Tristani-Firouzi
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA; Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Eloy G Moreno-Galindo
- Centro Universitario de Investigaciones Biomédicas, Universidad de Colima, Colima, Col., Mexico.
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Pathways for the formation of ice polymorphs from water predicted by a metadynamics method. Sci Rep 2020; 10:4708. [PMID: 32170179 PMCID: PMC7069948 DOI: 10.1038/s41598-020-61773-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 02/28/2020] [Indexed: 02/07/2023] Open
Abstract
The mechanism of how ice crystal form has been extensively studied by many researchers but remains an open question. Molecular dynamics (MD) simulations are a useful tool for investigating the molecular-scale mechanism of crystal formation. However, the timescale of phenomena that can be analyzed by MD simulations is typically restricted to microseconds or less, which is far too short to explore ice crystal formation that occurs in real systems. In this study, a metadynamics (MTD) method was adopted to overcome this timescale limitation of MD simulations. An MD simulation combined with the MTD method, in which two discrete oxygen–oxygen radial distribution functions represented by Gaussian window functions were used as collective variables, successfully reproduced the formation of several different ice crystals when the Gaussian window functions were set at appropriate oxygen–oxygen distances: cubic ice, stacking disordered ice consisting of cubic ice and hexagonal ice, high-pressure ice VII, layered ice with an ice VII structure, and layered ice with an unknown structure. The free-energy landscape generated by the MTD method suggests that the formation of each ice crystal occurred via high-density water with a similar structure to the formed ice crystal. The present method can be used not only to study the mechanism of crystal formation but also to search for new crystals in real systems.
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39
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An X, Bai Q, Bing Z, Liu H, Zhang Q, Liu H, Yao X. Revealing the Positive Binding Cooperativity Mechanism between the Orthosteric and the Allosteric Antagonists of CCR2 by Metadynamics and Gaussian Accelerated Molecular Dynamics Simulations. ACS Chem Neurosci 2020; 11:628-637. [PMID: 31968162 DOI: 10.1021/acschemneuro.9b00630] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
CC chemokine receptor 2 (CCR2) and its endogenous CC chemokine ligands are associated with numerous inflammatory, neurodegenerative diseases, and cancer. CCR2 is becoming an attractive target in the treatment of autoimmune disease and neurodegenerative diseases. The orthosteric antagonist BMS-681 and allosteric antagonist CCR2-RA-[R] of CCR2 show positive binding cooperativity. We performed well-tempered metadynamics simulations and Gaussian accelerated MD simulations to reveal the influence of the orthosteric antagonist on the unbinding of allosteric antagonist of CCR2. We revealed different unbinding pathways of CCR2-RA-[R] in binary complex CCR2-VT5 and ternary complex CCR2-73R-VT5. The different unbinding pathways of CCR2-RA-[R] are due to the conformational dynamics of TM6. We obtained the significant conformational differences of the intracellular side of TM6 upon CCR2 binding to different ligands by GaMD simulation. The conformational dynamics of TM6 are consistent with the unbinding pathway analysis. GaMD simulations indicate that BMS-681 binding restricts the bend of intracellular side of TM6 by stabilizing the extracellular sides of TM6 and TM7. The charged residues Arg2065.43 of TM5 and Glu2917.39 of TM7 play key roles in stabling TM7 and TM6. TM6 and TM7 are crucial components in the orthosteric and allosteric binding sites. Our results illustrate the conformational details about the effect of the orthosteric antagonist on the allosteric antagonist of CCR2. The conformational dynamics of CCR2 upon binding to different ligands can provide a rational basis for development of allosteric ligands of CCR2.
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Affiliation(s)
- Xiaoli An
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou 730000, China
| | - Qifeng Bai
- School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China
| | - Zhitong Bing
- School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China
- Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
| | - Hongli Liu
- School of Pharmacy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Qianqian Zhang
- School of Pharmacy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Huanxiang Liu
- School of Pharmacy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Xiaojun Yao
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou 730000, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Taipa, Macau, China
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40
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Horx P, Geyer A. Comparing the Hinge-Type Mobility of Natural and Designed Intermolecular Bi-disulfide Domains. Front Chem 2020; 8:25. [PMID: 32047741 PMCID: PMC6997481 DOI: 10.3389/fchem.2020.00025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 01/09/2020] [Indexed: 01/10/2023] Open
Abstract
A pair of intermolecular disulfide bonds connecting two protein domains restricts their relative mobility in a systematic way. The bi-disulfide hinge cannot rotate like a single intermolecular disulfide bond yet is less restrained than three or more intermolecular disulfides which restrict the relative motion to a minimum. The intermediate mobility of bi-disulfide linked domains is characterized by their dominating opening and closing modes comparable to the mechanics of a door hinge on the macroscopic scale. Here we compare the central hinge region of Immunoglobulin G1 (IgG1) which is highly conserved among different species, with a recently designed hinge-type motif CHWECRGCRLVC from our lab, that was successfully used for the dimerization of the IgG1/κ-ab CL4 monocolonal antibody (mab). The minimal length of these synthetic hinges comprises only 12 amino acids, rendering them ideal models for computational studies. Well-tempered metadynamics was performed to adequately describe the available conformational space defined by the different hinges. In spite of the differences in amino acid composition and ring sizes, there are characteristic similarities of designed and natural hinges like the dependent mobility of the individual strands of each hinge domain.
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Affiliation(s)
- Philip Horx
- Faculty of Organic Chemistry, Philipps-University, Marburg, Germany
| | - Armin Geyer
- Faculty of Organic Chemistry, Philipps-University, Marburg, Germany
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41
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Muttathukattil AN, Srinivasan S, Halder A, Reddy G. Role of Guanidinium-Carboxylate Ion Interaction in Enzyme Inhibition with Implications for Drug Design. J Phys Chem B 2019; 123:9302-9311. [DOI: 10.1021/acs.jpcb.9b06130] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Aswathy N. Muttathukattil
- Solid State and Structural Chemistry Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Sriraksha Srinivasan
- Department of Chemistry, St. Joseph’s College, Bangalore, Karnataka 560027, India
| | - Antarip Halder
- Solid State and Structural Chemistry Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Govardhan Reddy
- Solid State and Structural Chemistry Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
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42
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Capelli R, Carloni P, Parrinello M. Exhaustive Search of Ligand Binding Pathways via Volume-Based Metadynamics. J Phys Chem Lett 2019; 10:3495-3499. [PMID: 31188006 DOI: 10.1021/acs.jpclett.9b01183] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Determining the complete set of ligands' binding-unbinding pathways is important for drug discovery and for rational interpretation of mutation data. Here we have developed a metadynamics-based technique that addresses this issue and allows estimating affinities in the presence of multiple escape pathways. Our approach is shown on a lysozyme T4 variant in complex with a benzene molecule. The calculated binding free energy is in agreement with experimental data. Remarkably, not only were we able to find all the previously identified ligand binding pathways, but also we identified three pathways previously not identified as such. These results were obtained at a small computational cost, making this approach valuable for practical applications, such as screening of small compound libraries.
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Affiliation(s)
- Riccardo Capelli
- Computational Biomedicine (INM-9/IAS-5) , Forschungszentrum Jülich , Wilhelm-Johnen-Straße , D-52425 Jülich , Germany
- JARA-HPC, Forschungszentrum Jülich , D-54245 Jülich , Germany
| | - Paolo Carloni
- Computational Biomedicine (INM-9/IAS-5) , Forschungszentrum Jülich , Wilhelm-Johnen-Straße , D-52425 Jülich , Germany
- Department of Physics , RWTH Aachen University , D-52078 Aachen , Germany
| | - Michele Parrinello
- Department of Chemistry and Applied Biosciences , ETH Zürich , c/o USI Campus, Via Giuseppe Buffi 13 , CH-6900 Lugano , Ticino Switzerland
- Facoltà di Informatica, Istituto di Scienze Computazionali , Università della Svizzera Italiana (USI) , Via Giuseppe Buffi 13 , CH-6900 Lugano , Ticino Switzerland
- Istituto Italiano di Tecnologia , Via Morego 30 , I-16163 Genova , Italy
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