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dc.citation.number 2 -
dc.citation.startPage 024001 -
dc.citation.title JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT -
dc.citation.volume 2024 -
dc.contributor.author Mcbride, John M. -
dc.contributor.author Tlusty, Tsvi -
dc.date.accessioned 2024-05-03T10:35:32Z -
dc.date.available 2024-05-03T10:35:32Z -
dc.date.created 2024-02-26 -
dc.date.issued 2024-02 -
dc.description.abstract Proteins are intricate molecular machines whose complexity arises from the heterogeneity of the amino acid building blocks and their dynamic network of many-body interactions. These nanomachines gain function when put in the context of a whole organism through interaction with other inhabitants of the biological realm. And this functionality shapes their evolutionary histories through intertwined paths of selection and adaptation. Recent advances in machine learning have solved the decades-old problem of how protein sequence determines their structure. However, the ultimate question regarding the basic logic of protein machines remains open: how does the collective physics of proteins lead to their functionality? and how does a sequence encode the full range of dynamics and chemical interactions that facilitate function? Here, we explore these questions within a physical approach that treats proteins as mechano-chemical machines, which are adapted to function via concerted evolution of structure, motion, and chemical interactions. -
dc.identifier.bibliographicCitation JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, v.2024, no.2, pp.024001 -
dc.identifier.doi 10.1088/1742-5468/ad1be7 -
dc.identifier.issn 1742-5468 -
dc.identifier.scopusid 2-s2.0-85185177341 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/82305 -
dc.identifier.wosid 001157461500001 -
dc.language 영어 -
dc.publisher IOP Publishing Ltd -
dc.title The physical logic of protein machines -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Mechanics; Physics, Mathematical -
dc.relation.journalResearchArea Mechanics; Physics -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor deep learning -
dc.subject.keywordAuthor biomolecules -
dc.subject.keywordAuthor protein function and design -
dc.subject.keywordAuthor computational biology -
dc.subject.keywordPlus CONFORMATIONAL SELECTION -
dc.subject.keywordPlus INDUCED-FIT -
dc.subject.keywordPlus SIGNAL-TRANSDUCTION -
dc.subject.keywordPlus ENZYME SPECIFICITY -
dc.subject.keywordPlus SINGLE-PARAMETER -
dc.subject.keywordPlus COUPLED BINDING -
dc.subject.keywordPlus EVOLUTION -
dc.subject.keywordPlus DYNAMICS -
dc.subject.keywordPlus PROMISCUITY -
dc.subject.keywordPlus STABILITY -

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