Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1178194
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorOMAGE, F. B.
dc.contributor.authorSALIM, J. A.
dc.contributor.authorMAZONI, I.
dc.contributor.authorYANO, I. H.
dc.contributor.authorGONZÁLEZ, J. E. H.
dc.contributor.authorGIACHETTO, P. F.
dc.contributor.authorTASIC, L.
dc.contributor.authorARNI, R. K.
dc.contributor.authorNESHICH, G.
dc.date.accessioned2025-08-21T14:48:49Z-
dc.date.available2025-08-21T14:48:49Z-
dc.date.created2025-08-21
dc.date.issued2025
dc.identifier.citationBriefings in Bioinformatics, v. 26, n. 4, bbaf424, 2025.
dc.identifier.issn1467-5463
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1178194-
dc.descriptionAllosteric 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectRegulação alostérica
dc.subjectPrevisão de sítio alostérico
dc.subjectNanoambiente de proteína
dc.subjectAprendizado de máquina
dc.subjectWeb server
dc.subjectAllosteric regulation
dc.subjectAllosteric site prediction
dc.subjectInternal protein nanoenvironment
dc.subjectMachine learning
dc.subjectAllosteric site-forming residues
dc.subjectPer-residue classification
dc.subjectSTING most relevant descriptors for IPNs
dc.titleSTINGAllo: a web server for high-throughput prediction of allosteric site-forming residues using internal protein nanoenvironment descriptors.
dc.typeArtigo de periódico
riaa.ainfo.id1178194
riaa.ainfo.lastupdate2025-08-21
dc.identifier.doihttps://doi.org/10.1093/bib/bbaf424
dc.contributor.institutionFOLORUNSHO 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.
Aparece nas coleções:Artigo em periódico indexado (CNPTIA)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
Ap-STINGAllo-2025.pdf1.55 MBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace