Work

Structure-mechanics statistical learning uncovers mechanical relay in proteins

Year: 2022

Type: article

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.

Source: Chemical Science

DOI: 10.1039/d1sc06184d

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

Institutions: National Yang Ming Chiao Tung University, Princeton University

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: diamond

Funders: Princeton University, Ministry of Science and Technology, Taiwan

Award: Mechanistic Analysis of Functional Activity for Three-Prime Repair Exonuclease 1 (Trex1) by Molecular Simulation and Structure-Mechanics Statistical Learning( II )