STINGAllo: a web server for high-throughput prediction of allosteric site-forming residues using internal protein nanoenvironment descriptors.

ABSTRACT: Allosteric regulation is essential for modulating protein function and represents a promising target for therapeutic intervention, yet the complex dynamics of the protein nanoenvironment hinder the reliable identification of allosteric sites. Traditional pocket-based predictors miss $\sim $18% of experimentally confirmed sites that lie outside surface invaginations. To overcome this limitation, we developed STINGAllo, an interactive web server that introduces a residue-centric machine-learning model. Using 54 optimized internal protein nanoenvironment descriptors, STINGAllo predicts allosteric site-forming residues at single-residue resolution. By integrating hydrophobic interaction networks, local density, graph connectivity, and a unique "sponge effect" metric, STINGAllo detects alloster

SUBMITTER: Omage FB 

PROVIDER: S-EPMC12368853 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature