Título:	STINGAllo: a web server for high-throughput prediction of allosteric site-forming residues using internal protein nanoenvironment descriptors.
Autor:	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.
Afiliación:	FOLORUNSHO BRIGHT OMAGE; JOSÉ AUGUSTO SALIM, UNIVERSIDADE ESTADUAL DE CAMPINAS; IVAN MAZONI, CNPTIA; INACIO HENRIQUE YANO, CNPTIA; JORGE ENRIQUE HERNÁNDEZ GONZÁLEZ, UNIVERSIDADE ESTADUAL PAULISTA "JÚLIO DE MESQUITA FILHO"; POLIANA FERNANDA GIACHETTO, CNPTIA; LJUBICA TASIC, UNIVERSIDADE ESTADUAL DE CAMPINAS; RAGHUVIR KRISHNASWAMY ARNI, UNIVERSIDADE ESTADUAL PAULISTA "JÚLIO DE MESQUITA FILHO"; GORAN NESIC, CNPTIA.
Año:	2025
Referencia:	Briefings in Bioinformatics, v. 26, n. 4, bbaf424, 2025.
Descripción:	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.
Palabras clave:	Regulação alostérica Previsão de sítio alostérico Nanoambiente de proteína Aprendizado de máquina Web server Allosteric regulation Allosteric site prediction Internal protein nanoenvironment Machine learning Allosteric site-forming residues Per-residue classification STING most relevant descriptors for IPNs
ISSN:	1467-5463
DOI:	https://doi.org/10.1093/bib/bbaf424
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPTIA)