Please use this identifier to cite or link to this item:
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1060954
Research center of Embrapa/Collection: | Embrapa Informática Agropecuária - Resumo em anais de congresso (ALICE) |
Date Issued: | 2016 |
Type of Material: | Resumo em anais de congresso (ALICE) |
Authors: | BORRO, L.![]() ![]() YANO, I. H. ![]() ![]() MAZONI, I. ![]() ![]() NESHICH, G. ![]() ![]() |
Additional Information: | LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA. |
Title: | Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions. |
Publisher: | In: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s.n.], 2016. |
Pages: | p. 116-117. |
Language: | en |
Notes: | 3Dsig 2016. Pôster #56. |
Keywords: | Interações entre proteína e ligantes Modelagem Modelos Complexo proteína-ligante Protein-ligand complex Binding affinity prediction model Empiric nonparametric predictive model Plataforma Sting |
Description: | 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. |
NAL Thesaurus: | Binding properties Models |
Data Created: | 2017-01-17 |
Appears in Collections: | Resumo em anais de congresso (CNPTIA)![]() ![]() |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
PL3DSIG2016BindingBorro.pdf | 570.59 kB | Adobe PDF | ![]() View/Open |