ArticleChemical science2022
Structure-mechanics statistical learning uncovers mechanical relay in proteins.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.The journal of physical chemistry. B · 2024Review
- Mechanical codes of chemical-scale specificity in DNA motifs.Chemical science · 2023Article
- Statistical learning of protein elastic network from positional covariance matrix.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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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.
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Registered trials
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