Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins.

ABSTRACT: A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allo

SUBMITTER: Leander M 

PROVIDER: S-EPMC9662819 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature