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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
11/11/2024 |
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Data da última atualização: |
18/11/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OMAGE, F. B.; SALIM, J. A.; MAZONI, I.; YANO, I. H.; BORRO, L.; GONZALEZ, J. E. H.; MORAES, F. R. de; GIACHETTO, P. F.; TASIC, L.; ARNI, R. K.; NESHICH, G. |
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Afiliação: |
FOLORUNSHO BRIGHT OMAGE, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOSÉ AUGUSTO SALIM, UNIVERSIDADE ESTADUAL DE CAMPINAS; IVAN MAZONI, CNPTIA; INACIO HENRIQUE YANO, CNPTIA; LUIZ BORRO, UNIVERSIDADE ESTADUAL DE CAMPINAS; JORGE ENRIQUE HERNÁNDEZ GONZALEZ, UNIVERSIDADE ESTADUAL PAULISTA "JÚLIO DE MESQUITA FILHO"; FABIO ROGERIO DE MORAES, 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. |
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Título: |
Protein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Computational and Structural Biotechnology, v. 23, p. 3907-3919, Dec. 2024. |
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ISSN: |
2001-0370 |
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DOI: |
https://doi.org/10.1016/j.csbj.2024.10.036 |
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Idioma: |
Inglês |
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Conteúdo: |
Allosteric 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 inhi- bition. 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), hydro- phobic interactions, electrostatic potentials, eccentricity, and graph bottleneck features. |
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Palavras-Chave: |
Allosteric sites; Análise da estrutura proteica; Aprendizado de máquina; Computational drug design; Descritores STING; Distance center center; Internal protein nanoenvironment; Machine learning; Nanoambiente interno de proteínas; Protein structure analysis; Regulação alostérica; Sitios alostéricos; STING descriptors. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02679naa a2200421 a 4500
001 2169021
005 2024-11-18
008 2024 bl uuuu u00u1 u #d
022 $a2001-0370
024 7 $ahttps://doi.org/10.1016/j.csbj.2024.10.036$2DOI
100 1 $aOMAGE, F. B.
245 $aProtein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors.$h[electronic resource]
260 $c2024
520 $aAllosteric 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 inhi- bition. 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), hydro- phobic interactions, electrostatic potentials, eccentricity, and graph bottleneck features.
653 $aAllosteric sites
653 $aAnálise da estrutura proteica
653 $aAprendizado de máquina
653 $aComputational drug design
653 $aDescritores STING
653 $aDistance center center
653 $aInternal protein nanoenvironment
653 $aMachine learning
653 $aNanoambiente interno de proteínas
653 $aProtein structure analysis
653 $aRegulação alostérica
653 $aSitios alostéricos
653 $aSTING descriptors
700 1 $aSALIM, J. A.
700 1 $aMAZONI, I.
700 1 $aYANO, I. H.
700 1 $aBORRO, L.
700 1 $aGONZALEZ, J. E. H.
700 1 $aMORAES, F. R. de
700 1 $aGIACHETTO, P. F.
700 1 $aTASIC, L.
700 1 $aARNI, R. K.
700 1 $aNESHICH, G.
773 $tComputational and Structural Biotechnology$gv. 23, p. 3907-3919, Dec. 2024.
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| 1. |  | MARTEL, C. M.; WARRILOW, A. G. S.; JACKSON, C. J.; MULLINS, J. G. L.; TOGAWA, R. C.; PARKER, J. E.; MORRIS, M. S.; DONNISON, I. S.; KELLY, D. E.; KELLY, S. L. Expression, purification and use of the soluble domain of Lactobacillus paracasei beta-fructosidase to optimise production of bioethanol from grass fructans. Bioresource Technology, v. 101, p. 4395-4402, 2010.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
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