Issue 13, 2022

Structure-mechanics statistical learning uncovers mechanical relay in proteins


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.

Graphical abstract: Structure-mechanics statistical learning uncovers mechanical relay in proteins

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Article information

Article type
Edge Article
08 Nov 2021
10 Jan 2022
First published
19 Jan 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2022,13, 3688-3696

Structure-mechanics statistical learning uncovers mechanical relay in proteins

N. Raj, T. H. Click, H. Yang and J. Chu, Chem. Sci., 2022, 13, 3688 DOI: 10.1039/D1SC06184D

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