Autoria: OMAGE, F. B.; SALIM, J. A.; MAZONI, I.; YANO, I. H.; GONZÁLEZ, J. E. H.; GIACHETTO, P. F.; TASIC, L.; ARNI, R. K.; NESHICH, G.

Resumo: 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 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 allosteric sites independently of surface geometry, including concave pockets, flat surfaces, or even cryptic regions. It achieves a success rate of 78% on benchmark datasets, substantially outperforming existing methods with a 60.2% overall success rate compared with 21.1%–24.2% for contemporary pocket-based predictors. Our analysis further reveals that nearly 52.7% of unique proteins in the Protein Data Bank [(PDB); 119 851 entries, 14 November 2024] contain at least one chain with a predicted allosteric site. STINGAllo accepts protein structures via PDB identifiers or custom uploads, provides interactive 3D visualization of predicted pockets, and supports integration into computational pipelines through a RESTful application programming interface. Overall, STINGAllo bridges advanced computational prediction with user-friendly design, offering a robust tool expected to deepen understanding of protein regulation and accelerate allosteric drug discovery.

Ano de publicação: 2025

Tipo de publicação: Artigo de periódico

Unidade: Embrapa Agricultura Digital

Palavras-chave: Allosteric regulation, Allosteric site prediction, Allosteric site-forming residues, Aprendizado de máquina, Internal protein nanoenvironment, Machine learning, Nanoambiente de proteína, Per-residue classification, Previsão de sítio alostérico, Regulação alostérica, STING most relevant descriptors for IPNs, Web server