Protein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorOmage, Folorunsho Bright-
Autor(es): dc.creatorSalim, José Augusto-
Autor(es): dc.creatorMazoni, Ivan-
Autor(es): dc.creatorYano, Inácio Henrique-
Autor(es): dc.creatorBorro, Luiz-
Autor(es): dc.creatorGonzalez, Jorge Enrique Hernández-
Autor(es): dc.creatorde Moraes, Fabio Rogerio-
Autor(es): dc.creatorGiachetto, Poliana Fernanda-
Autor(es): dc.creatorTasic, Ljubica-
Autor(es): dc.creatorArni, Raghuvir Krishnaswamy-
Autor(es): dc.creatorNeshich, Goran-
Data de aceite: dc.date.accessioned2025-08-21T18:00:16Z-
Data de disponibilização: dc.date.available2025-08-21T18:00:16Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.csbj.2024.10.036-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298458-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298458-
Descrição: dc.descriptionAllosteric regulation plays a crucial role in modulating protein functions and represents a promising strategy in drug development, offering enhanced specificity and reduced toxicity compared to traditional active site inhibition. Existing computational methods for predicting allosteric sites on proteins often rely on static protein surface pocket features, normal mode analysis or extensive molecular dynamics simulations encompassing both the protein function modulator and the protein itself. In this study, we introduce an innovative methodology that employs a per amino acid residue classifier to distinguish allosteric site-forming residues (AFRs) from non-allosteric, or free residues (FRs). Our model, STINGAllo, exhibits robust performance, achieving Distance Center Center (DCC) success rate when all AFRs were predicted within pockets identified by FPocket, overall DCC, F1 score and a Matthews correlation coefficient (MCC) of 78 %, 60 %, 64 % and 64 % respectively. Furthermore, we identified key descriptors that characterize the internal protein nanoenvironment of AFRs, setting them apart from FRs. These descriptors include the sponge effect, distance to the protein centre of geometry (cg), hydrophobic interactions, electrostatic potentials, eccentricity, and graph bottleneck features.-
Descrição: dc.descriptionComputational Biology Research Group Embrapa Digital Agriculture, São Paulo-
Descrição: dc.descriptionBiological Chemistry Laboratory Department of Organic Chemistry Institute of Chemistry University of Campinas (UNICAMP), São Paulo-
Descrição: dc.descriptionDepartment of Plant Biology Institute of Biology University of Campinas (UNICAMP), São Paulo-
Descrição: dc.descriptionSão Paulo State University (UNESP) Institute of Biosciences Humanities and Exact Sciences-
Descrição: dc.descriptionSão Paulo State University (UNESP) Institute of Biosciences Humanities and Exact Sciences-
Formato: dc.format3907-3919-
Idioma: dc.languageen-
Relação: dc.relationComputational and Structural Biotechnology Journal-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAllosteric sites-
Palavras-chave: dc.subjectComputational drug design-
Palavras-chave: dc.subjectDistance center center-
Palavras-chave: dc.subjectInternal protein nanoenvironment-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectProtein structure analysis-
Palavras-chave: dc.subjectSTING descriptors-
Título: dc.titleProtein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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