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DC Field | Value | Language |
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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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