{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T02:27:38Z","timestamp":1794018458599,"version":"4.8.0"},"reference-count":119,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T00:00:00Z","timestamp":1710547200000},"content-version":"am","delay-in-days":350,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R15GM122013"],"award-info":[{"award-number":["R15GM122013"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmn.com","cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Trends in Biochemical Sciences"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1016\/j.tibs.2022.12.001","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T08:15:59Z","timestamp":1671610559000},"page":"375-390","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":38,"title":["Machine learning and protein allostery"],"prefix":"10.1016","volume":"48","author":[{"given":"Sian","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gennady M.","family":"Verkhivker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-0239","authenticated-orcid":false,"given":"Peng","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.tibs.2022.12.001_bb0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmb.2022.167481","article-title":"Explaining and predicting allostery with allosteric database and modern analytical techniques","volume":"434","author":"Zha","year":"2022","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0010","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1021\/acs.accounts.8b00570","article-title":"Allosteric methods and their applications: facilitating the discovery of allosteric drugs and the investigation of allosteric mechanisms","volume":"52","author":"Lu","year":"2019","journal-title":"Acc. Chem. Res."},{"key":"10.1016\/j.tibs.2022.12.001_bb0015","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004966","article-title":"Allostery: an overview of its history, concepts, methods, and applications","volume":"12","author":"Liu","year":"2016","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0020","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1101\/SQB.1961.026.01.048","article-title":"General conclusions: teleonomic mechanisms in cellular metabolism, growth, and differentiation","volume":"26","author":"Monod","year":"1961","journal-title":"Cold Spring Harb. Symp. Quant. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0025","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.tibs.2008.05.009","article-title":"Allostery: an illustrated definition for the \u2018second secret of life\u2019","volume":"33","author":"Fenton","year":"2008","journal-title":"Trends Biochem. Sci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0030","series-title":"Chance and Necessity: An Essay on the Natural Philosophy of Modern Biology","author":"Monod","year":"1971"},{"key":"10.1016\/j.tibs.2022.12.001_bb0035","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1038\/nature13001","article-title":"The ensemble nature of allostery","volume":"508","author":"Motlagh","year":"2014","journal-title":"Nature"},{"key":"10.1016\/j.tibs.2022.12.001_bb0040","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1038\/nsmb.1978","article-title":"Competing allosteric mechanisms modulate substrate binding in a dimeric enzyme","volume":"18","author":"Freiburger","year":"2011","journal-title":"Nat. Struct. Mol. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0045","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1146\/annurev-biophys-083012-130257","article-title":"The (still) underappreciated role of allostery in the cellular network","volume":"42","author":"Nussinov","year":"2013","journal-title":"Annu. Rev. Biophys."},{"key":"10.1016\/j.tibs.2022.12.001_bb0050","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/prot.20232","article-title":"Is allostery an intrinsic property of all dynamic proteins?","volume":"57","author":"Gunasekaran","year":"2004","journal-title":"Proteins Struct. Funct. Bioinforma."},{"key":"10.1016\/j.tibs.2022.12.001_bb0055","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmb.2008.02.034","article-title":"Allostery: absence of a change in shape does not imply that allostery is not at play","volume":"378","author":"Tsai","year":"2008","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0060","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004620","article-title":"NMR methods to study dynamic allostery","volume":"12","author":"Grutsch","year":"2016","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0065","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1016\/j.cell.2016.05.040","article-title":"Breaking cryo-EM resolution barriers to facilitate drug discovery","volume":"165","author":"Merk","year":"2016","journal-title":"Cell"},{"key":"10.1016\/j.tibs.2022.12.001_bb0070","doi-asserted-by":"crossref","first-page":"764","DOI":"10.3390\/biom12060764","article-title":"Allostery modulates interactions between proteasome core particles and regulatory particles","volume":"12","author":"Coffino","year":"2022","journal-title":"Biomolecules"},{"key":"10.1016\/j.tibs.2022.12.001_bb0075","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aav4322","article-title":"Cryo-EM structure of phosphodiesterase 6 reveals insights into the allosteric regulation of type I phosphodiesterases","volume":"5","author":"Gulati","year":"2019","journal-title":"Sci. Adv."},{"key":"10.1016\/j.tibs.2022.12.001_bb0080","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1021\/acs.biochem.7b01094","article-title":"Systems approaches to understanding and designing allosteric proteins","volume":"57","author":"Raman","year":"2018","journal-title":"Biochemistry"},{"key":"10.1016\/j.tibs.2022.12.001_bb0085","doi-asserted-by":"crossref","first-page":"322","DOI":"10.2142\/biophysico.16.0_322","article-title":"Normal mode analysis and beyond","volume":"16","author":"Yamato","year":"2019","journal-title":"Biophys. Physicobiol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0090","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/j.neuron.2018.08.011","article-title":"Molecular dynamics simulation for all","volume":"99","author":"Hollingsworth","year":"2018","journal-title":"Neuron"},{"key":"10.1016\/j.tibs.2022.12.001_bb0095","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1002\/prot.24571","article-title":"Bridging between normal mode analysis and elastic network models","volume":"82","author":"Na","year":"2014","journal-title":"Proteins Struct. Funct. Bioinforma."},{"key":"10.1016\/j.tibs.2022.12.001_bb0100","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.str.2019.01.003","article-title":"Allostery in its many disguises: from theory to applications","volume":"27","author":"Wodak","year":"2019","journal-title":"Structure"},{"key":"10.1016\/j.tibs.2022.12.001_bb0105","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1038\/s41592-019-0496-6","article-title":"Machine-learning-guided directed evolution for protein engineering","volume":"16","author":"Yang","year":"2019","journal-title":"Nat. Methods"},{"key":"10.1016\/j.tibs.2022.12.001_bb0110","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","article-title":"Machine learning for molecular and materials science","volume":"559","author":"Butler","year":"2018","journal-title":"Nature"},{"key":"10.1016\/j.tibs.2022.12.001_bb0115","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1021\/acs.jcim.9b00266","article-title":"Deep learning in chemistry","volume":"59","author":"Mater","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0120","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1016\/j.bbagen.2017.09.005","article-title":"Identification of potential allosteric communication pathways between functional sites of the bacterial ribosome by graph and elastic network models","volume":"1861","author":"Guzel","year":"2017","journal-title":"Biochim. Biophys. Acta BBA Gen. Subj."},{"key":"10.1016\/j.tibs.2022.12.001_bb0125","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1000526","article-title":"Perturbation-based Markovian transmission model for probing allosteric dynamics of large macromolecular assembling: a study of GroEL-GroES","volume":"5","author":"Lu","year":"2009","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0130","doi-asserted-by":"crossref","first-page":"4587","DOI":"10.1021\/acs.jproteome.0c00654","article-title":"Molecular simulations and network modeling reveal an allosteric signaling in the SARS-CoV-2 spike proteins","volume":"19","author":"Verkhivker","year":"2020","journal-title":"J. Proteome Res."},{"key":"10.1016\/j.tibs.2022.12.001_bb0135","doi-asserted-by":"crossref","first-page":"156","DOI":"10.3389\/fmolb.2020.00156","article-title":"A time-dependent quantum approach to allostery and a comparison with light-harvesting in photosynthetic phenomenon","volume":"7","author":"Villani","year":"2020","journal-title":"Front. Mol. Biosci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0140","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.molcel.2020.11.032","article-title":"Dissecting the structural dynamics of the nuclear pore complex","volume":"81","author":"Hakhverdyan","year":"2021","journal-title":"Mol. Cell"},{"key":"10.1016\/j.tibs.2022.12.001_bb0145","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2012423118","article-title":"A method of incorporating rate constants as kinetic constraints in molecular dynamics simulations","volume":"118","author":"Brotzakis","year":"2021","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.tibs.2022.12.001_bb0150","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.32668","article-title":"Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning","volume":"7","author":"Matsunaga","year":"2018","journal-title":"eLife"},{"key":"10.1016\/j.tibs.2022.12.001_bb0155","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.sbi.2019.12.022","article-title":"Use of single-molecule time-series data for refining conformational dynamics in molecular simulations","volume":"61","author":"Matsunaga","year":"2020","journal-title":"Curr. Opin. Struct. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0160","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1021\/acs.jctc.7b01064","article-title":"Multiple pathways and time scales for conformational transitions in apo-adenylate kinase","volume":"14","author":"Zheng","year":"2018","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.tibs.2022.12.001_bb0165","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.60404","article-title":"DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning","volume":"9","author":"Thomsen","year":"2020","journal-title":"eLife"},{"key":"10.1016\/j.tibs.2022.12.001_bb0170","doi-asserted-by":"crossref","DOI":"10.1063\/1.5011399","article-title":"Time-lagged autoencoders: deep learning of slow collective variables for molecular kinetics","volume":"148","author":"Wehmeyer","year":"2018","journal-title":"J. Chem. Phys."},{"key":"10.1016\/j.tibs.2022.12.001_bb0175","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.97.062412","article-title":"Variational encoding of complex dynamics","volume":"97","author":"Hern\u00e1ndez","year":"2018","journal-title":"Phys. Rev. E"},{"key":"10.1016\/j.tibs.2022.12.001_bb0180","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41467-017-02388-1","article-title":"VAMPnets for deep learning of molecular kinetics","volume":"9","author":"Mardt","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.tibs.2022.12.001_bb0185","doi-asserted-by":"crossref","DOI":"10.1063\/1.5025487","article-title":"Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)","volume":"149","author":"Ribeiro","year":"2018","journal-title":"J. Chem. Phys."},{"key":"10.1016\/j.tibs.2022.12.001_bb0190","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.1021\/acs.jctc.7b00182","article-title":"tICA-Metadynamics: accelerating metadynamics by using kinetically selected collective variables","volume":"13","author":"Sultan","year":"2017","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.tibs.2022.12.001_bb0195","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.1021\/ct400919u","article-title":"On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations","volume":"10","author":"Doerr","year":"2014","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.tibs.2022.12.001_bb0200","doi-asserted-by":"crossref","first-page":"5747","DOI":"10.1021\/acs.jctc.5b00737","article-title":"FAST conformational searches by balancing exploration\/exploitation trade-offs","volume":"11","author":"Zimmerman","year":"2015","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.tibs.2022.12.001_bb0205","doi-asserted-by":"crossref","DOI":"10.1126\/science.aaw1147","article-title":"Boltzmann generators: sampling equilibrium states of many-body systems with deep learning","volume":"365","author":"No\u00e9","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.tibs.2022.12.001_bb0210","doi-asserted-by":"crossref","first-page":"154","DOI":"10.2477\/jccj.2021-0014","article-title":"Performance research of clustering methods for detecting state transition trajectories in hemoglobin","volume":"19","author":"Takami","year":"2020","journal-title":"J. Comput. Chem. Jpn."},{"key":"10.1016\/j.tibs.2022.12.001_bb0215","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1021\/jacsau.1c00254","article-title":"Markov state models to study the functional dynamics of proteins in the wake of machine learning","volume":"1","author":"Konovalov","year":"2021","journal-title":"JACS Au"},{"key":"10.1016\/j.tibs.2022.12.001_bb0220","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2113533118","article-title":"Deep learning the slow modes for rare events sampling","volume":"118","author":"Bonati","year":"2021","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.tibs.2022.12.001_bb0225","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1021\/acs.jpclett.8b00759","article-title":"Machine learning of biomolecular reaction coordinates","volume":"9","author":"Brandt","year":"2018","journal-title":"J. Phys. Chem. Lett."},{"key":"10.1016\/j.tibs.2022.12.001_bb0230","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1002\/jcc.25218","article-title":"Recognition of protein allosteric states and residues: machine learning approaches","volume":"39","author":"Zhou","year":"2018","journal-title":"J. Comput. Chem."},{"key":"10.1016\/j.tibs.2022.12.001_bb0235","doi-asserted-by":"crossref","first-page":"4691","DOI":"10.1021\/acs.jcim.9b00447","article-title":"Probing protein allostery as a residue-specific concept via residue response maps","volume":"59","author":"Hayatshahi","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0240","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1021\/acs.jctc.1c01055","article-title":"GLOW: a workflow integrating gaussian-accelerated molecular dynamics and deep learning for free energy profiling","volume":"18","author":"Do","year":"2022","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.tibs.2022.12.001_bb0245","doi-asserted-by":"crossref","first-page":"8308","DOI":"10.1038\/ncomms9308","article-title":"Pathways of allosteric regulation in Hsp70 chaperones","volume":"6","author":"Kityk","year":"2015","journal-title":"Nat. Commun."},{"key":"10.1016\/j.tibs.2022.12.001_bb0250","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1038\/nature20137","article-title":"Alternative modes of client binding enable functional plasticity of Hsp70","volume":"539","author":"Mashaghi","year":"2016","journal-title":"Nature"},{"key":"10.1016\/j.tibs.2022.12.001_bb0255","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.molcel.2017.12.003","article-title":"Molecular mechanism of J-domain-triggered ATP hydrolysis by Hsp70 chaperones","volume":"69","author":"Kityk","year":"2018","journal-title":"Mol. Cell"},{"key":"10.1016\/j.tibs.2022.12.001_bb0260","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1038\/s41467-022-29331-3","article-title":"Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations","volume":"13","author":"Zhu","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.tibs.2022.12.001_bb0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmb.2022.167518","article-title":"Emerging methods and applications to decrypt allostery in proteins and nucleic acids","volume":"434","author":"Arantes","year":"2022","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0270","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1080\/00268976.2018.1543904","article-title":"REDAN: relative entropy-based dynamical allosteric network model","volume":"117","author":"Zhou","year":"2019","journal-title":"Mol. Phys."},{"key":"10.1016\/j.tibs.2022.12.001_bb0275","doi-asserted-by":"crossref","DOI":"10.1063\/1.5022469","article-title":"Machine learning approaches to evaluate correlation patterns in allosteric signaling: a case study of the PDZ2 domain","volume":"148","author":"Botlani","year":"2018","journal-title":"J. Chem. Phys."},{"key":"10.1016\/j.tibs.2022.12.001_bb0280","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1021\/acs.jcim.8b00146","article-title":"Node-weighted amino acid network strategy for characterization and identification of protein functional residues","volume":"58","author":"Yan","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0285","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1006801","article-title":"Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis","volume":"15","author":"Zhou","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0290","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.csbj.2021.11.038","article-title":"Dynamics of hydrogen bonds in the secondary structures of allosteric protein Avena Sativa phototropin 1","volume":"20","author":"Ibrahim","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.tibs.2022.12.001_bb0295","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1005299","article-title":"Computational analysis of residue interaction networks and coevolutionary relationships in the Hsp70 chaperones: a community-hopping model of allosteric regulation and communication","volume":"13","author":"Stetz","year":"2017","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0300","doi-asserted-by":"crossref","first-page":"3616","DOI":"10.1021\/acs.jcim.0c00380","article-title":"Allosteric mechanism of the Hsp90 chaperone interactions with cochaperones and client proteins by modulating communication spines of coupled regulatory switches: integrative atomistic modeling of Hsp90 signaling in dynamic interaction networks","volume":"60","author":"Astl","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0305","doi-asserted-by":"crossref","first-page":"4706","DOI":"10.1021\/acs.jctc.0c00280","volume":"16","author":"Stetz","year":"2020","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.tibs.2022.12.001_bb0310","doi-asserted-by":"crossref","first-page":"6656","DOI":"10.1021\/acs.jctc.0c00503","volume":"16","author":"Astl","year":"2020","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.tibs.2022.12.001_bb0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmb.2022.167506","volume":"434","author":"Verkhivker","year":"2022","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0320","doi-asserted-by":"crossref","first-page":"6024","DOI":"10.1021\/acs.jcim.1c00826","article-title":"Allosteric effect of nanobody binding on ligand-specific active states of the \u03b22 adrenergic receptor","volume":"61","author":"Chen","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0325","first-page":"D394","article-title":"Unraveling allosteric landscapes of allosterome with ASD","volume":"48","author":"Liu","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.tibs.2022.12.001_bb0330","doi-asserted-by":"crossref","first-page":"2598","DOI":"10.1093\/bioinformatics\/btv169","article-title":"ASBench: benchmarking sets for allosteric discovery","volume":"31","author":"Huang","year":"2015","journal-title":"Bioinformatics"},{"key":"10.1016\/j.tibs.2022.12.001_bb0335","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.str.2017.01.008","article-title":"Predicting protein dynamics and allostery using multi-protein atomic distance constraints","volume":"25","author":"Greener","year":"2017","journal-title":"Structure"},{"key":"10.1016\/j.tibs.2022.12.001_bb0340","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1021\/acs.jcim.1c01267","article-title":"Uncovering the dominant motion modes of allosteric regulation improves allosteric site prediction","volume":"62","author":"Xie","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0345","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1016\/j.drudis.2014.07.012","article-title":"Recent computational advances in the identification of allosteric sites in proteins","volume":"19","author":"Lu","year":"2014","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.tibs.2022.12.001_bb0350","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1111\/cbdd.13161","article-title":"ALLO: a tool to discriminate and prioritize allosteric pockets","volume":"91","author":"Akbar","year":"2018","journal-title":"Chem. Biol. Drug Des."},{"key":"10.1016\/j.tibs.2022.12.001_bb0355","doi-asserted-by":"crossref","DOI":"10.1088\/2632-2153\/abe6d6","article-title":"PASSer: prediction of allosteric sites server","volume":"2","author":"Tian","year":"2021","journal-title":"Mach. Learn. Sci. Technol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0360","doi-asserted-by":"crossref","DOI":"10.3389\/fmolb.2022.879251","article-title":"PASSer2.0: accurate prediction of protein allosteric sites through automated machine learning","volume":"9","author":"Xiao","year":"2022","journal-title":"Front. Mol. Biosci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0365","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1002\/minf.201500108","article-title":"A random forest model for predicting allosteric and functional sites on proteins","volume":"35","author":"Chen","year":"2016","journal-title":"Mol. Inform."},{"key":"10.1016\/j.tibs.2022.12.001_bb0370","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1093\/bioinformatics\/btt399","article-title":"Allosite: a method for predicting allosteric sites","volume":"29","author":"Huang","year":"2013","journal-title":"Bioinformatics"},{"key":"10.1016\/j.tibs.2022.12.001_bb0375","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1186\/s12859-015-0771-1","article-title":"AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis","volume":"16","author":"Greener","year":"2015","journal-title":"BMC Bioinforma."},{"key":"10.1016\/j.tibs.2022.12.001_bb0380","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1021\/acs.jcim.7b00014","article-title":"Improved method for the identification and validation of allosteric sites","volume":"57","author":"Song","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0385","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1002\/prot.25749","article-title":"Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites","volume":"87","author":"Mishra","year":"2019","journal-title":"Proteins"},{"key":"10.1016\/j.tibs.2022.12.001_bb0390","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1021\/acsomega.9b02697","article-title":"Computational analysis of crystallization additives for the identification of new allosteric sites","volume":"5","author":"Fogha","year":"2020","journal-title":"ACS Omega"},{"key":"10.1016\/j.tibs.2022.12.001_bb0395","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1003394","article-title":"A unified view of \u201chow allostery works\u201d","volume":"10","author":"Tsai","year":"2014","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0400","series-title":"Protein Allostery in Drug Discovery","first-page":"89","article-title":"Correlation between allosteric and orthosteric sites","author":"Zhang","year":"2019"},{"key":"10.1016\/j.tibs.2022.12.001_bb0405","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.tibs.2019.09.007","article-title":"Renaissance of allostery to disrupt protein kinase interactions","volume":"45","author":"Leroux","year":"2020","journal-title":"Trends Biochem. Sci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0410","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1006228","article-title":"Reversing allosteric communication: from detecting allosteric sites to inducing and tuning targeted allosteric response","volume":"14","author":"Tee","year":"2018","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0415","doi-asserted-by":"crossref","first-page":"17728","DOI":"10.1021\/acs.jmedchem.1c01695","article-title":"Harnessing reversed allosteric communication: a novel strategy for allosteric drug discovery","volume":"64","author":"Fan","year":"2021","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.tibs.2022.12.001_bb0420","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1039\/D0SC05131D","article-title":"Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy","volume":"12","author":"Ni","year":"2021","journal-title":"Chem. Sci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0425","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1021\/acs.jpcb.0c09742","article-title":"Machine learning of allosteric effects: the analysis of ligand-induced dynamics to predict functional effects in TRAP1","volume":"125","author":"Ferraro","year":"2021","journal-title":"J. Phys. Chem. B"},{"key":"10.1016\/j.tibs.2022.12.001_bb0430","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.79932","article-title":"Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins","volume":"11","author":"Leander","year":"2022","journal-title":"eLife"},{"key":"10.1016\/j.tibs.2022.12.001_bb0435","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/s41586-022-04586-4","article-title":"Mapping the energetic and allosteric landscapes of protein binding domains","volume":"604","author":"Faure","year":"2022","journal-title":"Nature"},{"key":"10.1016\/j.tibs.2022.12.001_bb0440","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.jmgm.2012.07.006","article-title":"Toward understanding the molecular basis for chemical allosteric modulator design","volume":"38","author":"Wang","year":"2012","journal-title":"J. Mol. Graph. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0445","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1005813","article-title":"Are there physicochemical differences between allosteric and competitive ligands?","volume":"13","author":"Smith","year":"2017","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0450","doi-asserted-by":"crossref","first-page":"870","DOI":"10.3390\/biom11060870","article-title":"Integrated multi-class classification and prediction of GPCR allosteric modulators by machine learning intelligence","volume":"11","author":"Hou","year":"2021","journal-title":"Biomolecules"},{"key":"10.1016\/j.tibs.2022.12.001_bb0455","doi-asserted-by":"crossref","first-page":"915","DOI":"10.3390\/cells11050915","article-title":"Artificial intelligent deep learning molecular generative modeling of scaffold-focused and cannabinoid CB2 target-specific small-molecule sublibraries","volume":"11","author":"Bian","year":"2022","journal-title":"Cells"},{"key":"10.1016\/j.tibs.2022.12.001_bb0460","doi-asserted-by":"crossref","first-page":"33864","DOI":"10.1021\/acsomega.1c05145","article-title":"Transformer-based generative model accelerating the development of novel BRAF inhibitors","volume":"6","author":"Yang","year":"2021","journal-title":"ACS Omega"},{"key":"10.1016\/j.tibs.2022.12.001_bb0465","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1039\/D2ME00051B","article-title":"Multimodal generative neural networks and molecular dynamics based identification of PDK1 PIF-pocket modulators","volume":"7","author":"Vennila","year":"2022","journal-title":"Mol. Syst. Des. Eng."},{"key":"10.1016\/j.tibs.2022.12.001_bb0470","doi-asserted-by":"crossref","first-page":"W451","DOI":"10.1093\/nar\/gky374","article-title":"AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses","volume":"46","author":"Huang","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.tibs.2022.12.001_bb0475","doi-asserted-by":"crossref","first-page":"8738","DOI":"10.1021\/acs.jmedchem.9b00867","article-title":"Machine learning models for accurate prediction of kinase inhibitors with different binding modes","volume":"63","author":"Miljkovi\u0107","year":"2020","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.tibs.2022.12.001_bb0480","doi-asserted-by":"crossref","first-page":"6899","DOI":"10.1039\/C8SC02634C","article-title":"Machine intelligence decrypts \u03b2-lapachone as an allosteric 5-lipoxygenase inhibitor","volume":"9","author":"Rodrigues","year":"2018","journal-title":"Chem. Sci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0485","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.bpj.2013.07.010","article-title":"Investigating models of protein function and allostery with a widespread mutational analysis of a light-activated protein","volume":"105","author":"Zayner","year":"2013","journal-title":"Biophys. J."},{"key":"10.1016\/j.tibs.2022.12.001_bb0490","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.jmb.2012.11.041","article-title":"Impact of mutations on the allosteric conformational equilibrium","volume":"425","author":"Weinkam","year":"2013","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0495","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1080\/07391102.2018.1445032","article-title":"Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning","volume":"37","author":"Xiao","year":"2019","journal-title":"J. Biomol. Struct. Dyn."},{"key":"10.1016\/j.tibs.2022.12.001_bb0500","article-title":"Distinct classes of potassium channels fused to GPCRs as electrical signaling biosensors","volume":"1","author":"Garc\u00eda-Fern\u00e1ndez","year":"2021","journal-title":"Cell Rep. Methods"},{"key":"10.1016\/j.tibs.2022.12.001_bb0505","doi-asserted-by":"crossref","DOI":"10.3389\/fmolb.2021.679915","article-title":"Substitution of a surface-exposed residue involved in an allosteric network enhances tryptophan synthase function in cells","volume":"8","author":"D\u2019Amico","year":"2021","journal-title":"Front. Mol. Biosci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0510","doi-asserted-by":"crossref","DOI":"10.1016\/j.bbagen.2019.07.008","article-title":"Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks","author":"Astl","year":"2019","journal-title":"Biochim. Biophys. Acta Gen. Subj."},{"key":"10.1016\/j.tibs.2022.12.001_bb0515","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2100943118","article-title":"Distant residues modulate conformational opening in SARS-CoV-2 spike protein","volume":"118","author":"Ray","year":"2021","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.tibs.2022.12.001_bb0520","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.3390\/ijms22041573","article-title":"Predicting potential SARS-COV-2 drugs\u2014in depth drug database screening using deep neural network framework SSnet, classical virtual screening and docking","volume":"22","author":"Karki","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0525","doi-asserted-by":"crossref","first-page":"204a","DOI":"10.1016\/j.bpj.2020.11.1393","article-title":"Mechanism and pathways of inhibitor binding to the human ACE2 receptor for SARS-CoV1\/2","volume":"120","author":"Bhattarai","year":"2021","journal-title":"Biophys. J."},{"key":"10.1016\/j.tibs.2022.12.001_bb0530","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1038\/s41569-020-0413-9","article-title":"COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives","volume":"17","author":"Nishiga","year":"2020","journal-title":"Nat. Rev. Cardiol."},{"key":"10.1016\/j.tibs.2022.12.001_bb0535","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1002\/prot.26086","article-title":"Molecular basis for higher affinity of SARS-CoV-2 spike RBD for human ACE2 receptor","volume":"89","author":"Delgado","year":"2021","journal-title":"Proteins Struct. Funct. Bioinforma."},{"key":"10.1016\/j.tibs.2022.12.001_bb0540","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.1039\/D2OB00606E","article-title":"Allosteric control of ACE2 peptidase domain dynamics","volume":"20","author":"Trozzi","year":"2022","journal-title":"Org. Biomol. Chem."},{"key":"10.1016\/j.tibs.2022.12.001_bb0545","doi-asserted-by":"crossref","first-page":"5896","DOI":"10.1021\/acs.jctc.1c00325","article-title":"Perturbation of ACE2 structural ensembles by SARS-CoV-2 spike protein binding","volume":"17","author":"Uyar","year":"2021","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.tibs.2022.12.001_bb0550","doi-asserted-by":"crossref","first-page":"204a","DOI":"10.1016\/j.bpj.2020.11.1391","article-title":"Prediction and analysis of multiple sites and inhibitors of SARS-CoV-2 proteins","volume":"120","author":"Iyengar","year":"2021","journal-title":"Biophys. J."},{"key":"10.1016\/j.tibs.2022.12.001_bb0555","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1021\/acsptsci.1c00176","article-title":"Hybrid In silico approach reveals novel inhibitors of multiple SARS-CoV-2 variants","volume":"4","author":"Jain","year":"2021","journal-title":"ACS Pharmacol. Transl. Sci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0560","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1016\/j.csbj.2022.06.023","article-title":"Maturation of the SARS-CoV-2 virus is regulated by dimerization of its main protease","volume":"20","author":"Kaptan","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.tibs.2022.12.001_bb0565","doi-asserted-by":"crossref","first-page":"26354","DOI":"10.1021\/acsomega.1c03558","article-title":"Atomistic simulations and in silico mutational profiling of protein stability and binding in the SARS-CoV-2 spike protein complexes with nanobodies: molecular determinants of mutational escape mechanisms","volume":"6","author":"Verkhivker","year":"2021","journal-title":"ACS Omega"},{"key":"10.1016\/j.tibs.2022.12.001_bb0570","doi-asserted-by":"crossref","first-page":"9724","DOI":"10.1080\/07391102.2021.1933594","article-title":"Computational analysis of protein stability and allosteric interaction networks in distinct conformational forms of the SARS-CoV-2 spike D614G mutant: reconciling functional mechanisms through allosteric model of spike regulation","volume":"40","author":"Verkhivker","year":"2021","journal-title":"J. Biomol. Struct. Dyn."},{"key":"10.1016\/j.tibs.2022.12.001_bb0575","doi-asserted-by":"crossref","first-page":"5172","DOI":"10.1021\/acs.jcim.1c00766","article-title":"Allosteric control of structural mimicry and mutational escape in the SARS-CoV-2 spike protein complexes with the ACE2 decoys and miniprotein inhibitors: a network-based approach for mutational profiling of binding and signaling","volume":"61","author":"Verkhivker","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.tibs.2022.12.001_bb0580","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1021\/acs.jpcb.0c10637","article-title":"Dynamic network modeling of allosteric interactions and communication pathways in the SARS-CoV-2 spike trimer mutants: differential modulation of conformational landscapes and signal transmission via cascades of regulatory switches","volume":"125","author":"Verkhivker","year":"2021","journal-title":"J. Phys. Chem. B"},{"key":"10.1016\/j.tibs.2022.12.001_bb0585","doi-asserted-by":"crossref","first-page":"2172","DOI":"10.3390\/ijms23042172","article-title":"Allosteric determinants of the SARS-CoV-2 spike protein binding with nanobodies: examining mechanisms of mutational escape and sensitivity of the omicron variant","volume":"23","author":"Verkhivker","year":"2022","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0590","doi-asserted-by":"crossref","first-page":"4376","DOI":"10.3390\/ijms23084376","article-title":"Computer simulations and network-based profiling of binding and allosteric interactions of SARS-CoV-2 spike variant complexes and the host receptor: dissecting the mechanistic effects of the delta and omicron mutations","volume":"23","author":"Verkhivker","year":"2022","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.tibs.2022.12.001_bb0595","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1021\/acs.jcim.2c00124","article-title":"Landscape-based protein stability analysis and network modeling of multiple conformational states of the SARS-CoV-2 spike D614G mutant: conformational plasticity and frustration-induced allostery as energetic drivers of highly transmissible spike variants","volume":"62","author":"Verkhivker","year":"2022","journal-title":"J. Chem. Inf. Model."}],"container-title":["Trends in Biochemical Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0968000422003115?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0968000422003115?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T21:47:15Z","timestamp":1760824035000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0968000422003115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4]]},"references-count":119,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["S0968000422003115"],"URL":"https:\/\/doi.org\/10.1016\/j.tibs.2022.12.001","relation":{},"ISSN":["0968-0004"],"issn-type":[{"value":"0968-0004","type":"print"}],"subject":[],"published":{"date-parts":[[2023,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning and protein allostery","name":"articletitle","label":"Article Title"},{"value":"Trends in Biochemical Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.tibs.2022.12.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}