Structure-mechanics statistical learning uncovers mechanical relay in proteins

Nixon Raj, Timothy H. Click, Haw Yang, Jhih Wei Chu

Research output: Contribution to journalArticlepeer-review

Abstract

A protein's adaptive response to its substrates is one of the key questions driving molecular physics and physical chemistry. This work employs the recently developed structure-mechanics statistical learning method to establish a mechanical perspective. Specifically, by mapping all-atom molecular dynamics simulations onto the spring parameters of a backbone-side-chain elastic network model, the chemical moiety specific force constants (or mechanical rigidity) are used to assemble the rigidity graph, which is the matrix of inter-residue coupling strength. Using the S1A protease and the PDZ3 signaling domain as examples, chains of spatially contiguous residues are found to exhibit prominent changes in their mechanical rigidity upon substrate binding or dissociation. Such a mechanical-relay picture thus provides a mechanistic underpinning for conformational changes, long-range communication, and inter-domain allostery in both proteins, where the responsive mechanical hotspots are mostly residues having important biological functions or significant mutation sensitivity.

Original languageEnglish (US)
Pages (from-to)3688-3696
Number of pages9
JournalChemical Science
Volume13
Issue number13
DOIs
StatePublished - Jan 19 2022

All Science Journal Classification (ASJC) codes

  • Chemistry(all)

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