Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
17/01/2017 |
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Data da última atualização: |
21/01/2020 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
BORRO, L.; YANO, I. H.; MAZONI, I.; NESHICH, G. |
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Afiliação: |
LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA. |
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Título: |
Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s.n.], 2016. |
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Páginas: |
p. 116-117. |
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Descrição Física: |
1 pôster. |
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Idioma: |
Inglês |
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Notas: |
3Dsig 2016. Pôster #56. |
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Conteúdo: |
We propose a new empirical scoring function for binding affinity prediction modeled based on physicochemical and structural descriptors that characterize the nano-environment that encompass both ligand and binding pocket residues. Our hypothesis is that a more detailed characterization of protein-ligand complexes in terms of describing nano-environment as precisely as possible can lead to improvements in binding affinity prediction. |
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Palavras-Chave: |
Binding affinity prediction model; Complexo proteína-ligante; Empiric nonparametric predictive model; Interações entre proteína e ligantes; Modelagem; Modelos; Plataforma Sting; Protein-ligand complex. |
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Thesaurus NAL: |
Binding properties; Models. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/153412/1/PL-3DSIG-2016-Binding-Borro.pdf
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Marc: |
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001 2060954
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008 2016 bl uuuu u00u1 u #d
100 1 $aBORRO, L.
245 $aBinding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.$h[electronic resource]
260 $aIn: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s.n.]$c2016
300 $ap. 116-117.$c1 pôster.
500 $a3Dsig 2016. Pôster #56.
520 $aWe propose a new empirical scoring function for binding affinity prediction modeled based on physicochemical and structural descriptors that characterize the nano-environment that encompass both ligand and binding pocket residues. Our hypothesis is that a more detailed characterization of protein-ligand complexes in terms of describing nano-environment as precisely as possible can lead to improvements in binding affinity prediction.
650 $aBinding properties
650 $aModels
653 $aBinding affinity prediction model
653 $aComplexo proteína-ligante
653 $aEmpiric nonparametric predictive model
653 $aInterações entre proteína e ligantes
653 $aModelagem
653 $aModelos
653 $aPlataforma Sting
653 $aProtein-ligand complex
700 1 $aYANO, I. H.
700 1 $aMAZONI, I.
700 1 $aNESHICH, G.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
