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Davolio AJ, J. Jankowski W, Várnai C, Irwin BWJ, Payne MC, Chau PL. Efficiently Differentiating Agonists and Competitive Antagonists for Weak Allosteric Protein-Ligand Interactions with Linear Response Theory. ACS OMEGA 2023; 8:44537-44544. [PMID: 38046342 PMCID: PMC10688131 DOI: 10.1021/acsomega.3c03503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/05/2023] [Indexed: 12/05/2023]
Abstract
What makes an agonist and a competitive antagonist? In this work, we aim to answer this question by performing parallel tempering Monte Carlo simulations on the serotonin type 3A (5-HT3A) receptor. We use linear response theory to predict conformational changes in the 5-HT3A receptor active site after weak perturbations are applied to its allosteric binding sites. A covariance tensor is built from conformational sampling of its apo state, and a harmonic approximation allows us to substitute the calculation of ligand-induced forces with the binding site's displacement vector. Remarkably, our study demonstrates the feasibility of effectively discerning between agonists and competitive antagonists for multiple ligands, requiring computationally expensive calculations only once per protein.
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Affiliation(s)
- Anthony J. Davolio
- Theory
of Condensed Matter Group, Department of Physics, University of Cambridge, Cambridge CB3 0HE, U.K.
| | - Wojciech J. Jankowski
- Theory
of Condensed Matter Group, Department of Physics, University of Cambridge, Cambridge CB3 0HE, U.K.
| | - Csilla Várnai
- Centre
for Computational Biology, University of
Birmingham, Birmingham B15 2TT, U.K.
- Institute
of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2SY, U.K.
| | - Benedict W. J. Irwin
- Theory
of Condensed Matter Group, Department of Physics, University of Cambridge, Cambridge CB3 0HE, U.K.
| | - Michael C. Payne
- Theory
of Condensed Matter Group, Department of Physics, University of Cambridge, Cambridge CB3 0HE, U.K.
| | - Pak-Lee Chau
- Bioinformatique
Structurale, Institut Pasteur, CNRS URA
3528, CB3I CNRS USR 3756, 75724 Paris, France
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Greener JG, Jones DT. Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins. PLoS One 2021; 16:e0256990. [PMID: 34473813 PMCID: PMC8412298 DOI: 10.1371/journal.pone.0256990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/19/2021] [Indexed: 11/26/2022] Open
Abstract
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.
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Affiliation(s)
- Joe G. Greener
- Department of Computer Science, University College London, London, United Kingdom
| | - David T. Jones
- Department of Computer Science, University College London, London, United Kingdom
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Zhang Y, Sanner MF. AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes. Bioinformatics 2020; 35:5121-5127. [PMID: 31161213 DOI: 10.1093/bioinformatics/btz459] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 04/09/2019] [Accepted: 05/29/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Protein-peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs. RESULTS Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein-peptide complex. We show that it outperforms leading peptide docking methods on two protein-peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein-peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein-protein interactions and interactions with disordered proteins. AVAILABILITY AND IMPLEMENTATION ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuqi Zhang
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Michel F Sanner
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
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Várnai C, Irwin BWJ, Payne MC, Csányi G, Chau PL. Functional movements of the GABA type A receptor. Phys Chem Chem Phys 2020; 22:16023-16031. [DOI: 10.1039/d0cp01128b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We have performed a Monte Carlo simulation of the GABA type A receptor. We have analysed the configurations and developed a correlation tensor method to predict receptor gating.
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Affiliation(s)
- Csilla Várnai
- Centre for Computational Biology
- University of Birmingham
- Birmingham
- UK
| | - B. W. J. Irwin
- Theory of Condensed Matter Group
- Cavendish Laboratory, Department of Physics
- University of Cambridge
- Cambridge CB3 0HE
- UK
| | - M. C. Payne
- Theory of Condensed Matter Group
- Cavendish Laboratory, Department of Physics
- University of Cambridge
- Cambridge CB3 0HE
- UK
| | - Gábor Csányi
- Department of Engineering
- University of Cambridge
- Cambridge CB2 1PZ
- UK
| | - P.-L. Chau
- Bioinformatique Structurale
- Institut Pasteur CNRS URA 3528
- CB3I CNRS USR 3756
- 75724 Paris
- France
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Zhang Y, Sanner MF. Docking Flexible Cyclic Peptides with AutoDock CrankPep. J Chem Theory Comput 2019; 15:5161-5168. [PMID: 31505931 DOI: 10.1021/acs.jctc.9b00557] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep (ADCP), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.
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Affiliation(s)
- Yuqi Zhang
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037 , United States
| | - Michel F Sanner
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037 , United States
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Jumper JM, Faruk NF, Freed KF, Sosnick TR. Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours. PLoS Comput Biol 2018; 14:e1006578. [PMID: 30589834 PMCID: PMC6307714 DOI: 10.1371/journal.pcbi.1006578] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 10/08/2018] [Indexed: 01/01/2023] Open
Abstract
An ongoing challenge in protein chemistry is to identify the underlying interaction energies that capture protein dynamics. The traditional trade-off in biomolecular simulation between accuracy and computational efficiency is predicated on the assumption that detailed force fields are typically well-parameterized, obtaining a significant fraction of possible accuracy. We re-examine this trade-off in the more realistic regime in which parameterization is a greater source of error than the level of detail in the force field. To address parameterization of coarse-grained force fields, we use the contrastive divergence technique from machine learning to train from simulations of 450 proteins. In our procedure, the computational efficiency of the model enables high accuracy through the precise tuning of the Boltzmann ensemble. This method is applied to our recently developed Upside model, where the free energy for side chains is rapidly calculated at every time-step, allowing for a smooth energy landscape without steric rattling of the side chains. After this contrastive divergence training, the model is able to de novo fold proteins up to 100 residues on a single core in days. This improved Upside model provides a starting point both for investigation of folding dynamics and as an inexpensive Bayesian prior for protein physics that can be integrated with additional experimental or bioinformatic data.
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Affiliation(s)
- John M. Jumper
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, USA
- Department of Chemistry, and The James Franck Institute, University of Chicago, Chicago, Illinois, USA
| | - Nabil F. Faruk
- Graduate Program in Biophysical Sciences, University of Chicago, Chicago, Illinois, USA
| | - Karl F. Freed
- Department of Chemistry, and The James Franck Institute, University of Chicago, Chicago, Illinois, USA
| | - Tobin R. Sosnick
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, USA
- Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois, USA
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7
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Jiang B, Wu TY, Jin Y, Wong WH. Convergence of contrastive divergence algorithm in exponential family. Ann Stat 2018. [DOI: 10.1214/17-aos1649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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8
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Várnai C, Burkoff NS, Wild DL. Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs. PLoS One 2017; 12:e0169356. [PMID: 28166227 PMCID: PMC5293240 DOI: 10.1371/journal.pone.0169356] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 12/15/2016] [Indexed: 01/05/2023] Open
Abstract
Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.
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Affiliation(s)
- Csilla Várnai
- Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Nikolas S. Burkoff
- Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - David L. Wild
- Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
- * E-mail:
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Thompson JJ, Tabatabaei Ghomi H, Lill MA. Application of information theory to a three-body coarse-grained representation of proteins in the PDB: insights into the structural and evolutionary roles of residues in protein structure. Proteins 2014; 82:3450-65. [PMID: 25269778 DOI: 10.1002/prot.24698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 09/09/2014] [Accepted: 09/19/2014] [Indexed: 01/03/2023]
Abstract
Knowledge-based methods for analyzing protein structures, such as statistical potentials, primarily consider the distances between pairs of bodies (atoms or groups of atoms). Considerations of several bodies simultaneously are generally used to characterize bonded structural elements or those in close contact with each other, but historically do not consider atoms that are not in direct contact with each other. In this report, we introduce an information-theoretic method for detecting and quantifying distance-dependent through-space multibody relationships between the sidechains of three residues. The technique introduced is capable of producing convergent and consistent results when applied to a sufficiently large database of randomly chosen, experimentally solved protein structures. The results of our study can be shown to reproduce established physico-chemical properties of residues as well as more recently discovered properties and interactions. These results offer insight into the numerous roles that residues play in protein structure, as well as relationships between residue function, protein structure, and evolution. The techniques and insights presented in this work should be useful in the future development of novel knowledge-based tools for the evaluation of protein structure.
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Affiliation(s)
- Jared J Thompson
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, West Lafayette, Indiana
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