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The physical logic of protein machines

Author(s)
Mcbride, John M.Tlusty, Tsvi
Issued Date
2024-02
DOI
10.1088/1742-5468/ad1be7
URI
https://scholarworks.unist.ac.kr/handle/201301/82305
Citation
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, v.2024, no.2, pp.024001
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.
Publisher
IOP Publishing Ltd
ISSN
1742-5468
Keyword (Author)
deep learningbiomoleculesprotein function and designcomputational biology
Keyword
CONFORMATIONAL SELECTIONINDUCED-FITSIGNAL-TRANSDUCTIONENZYME SPECIFICITYSINGLE-PARAMETERCOUPLED BINDINGEVOLUTIONDYNAMICSPROMISCUITYSTABILITY

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