Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.

BORRO, L.; YANO, I. H.; MAZONI, I.; NESHICH, G.; LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA.

Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.

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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Bibliographic Details
Main Authors:	BORRO, L., YANO, I. H., MAZONI, I., NESHICH, G.
Other Authors:	LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA.
Format:	Anais e Proceedings de eventos biblioteca
Language:	English eng
Published:	2017-01-17
Subjects:	Interações entre proteína e ligantes, Modelagem, Modelos, Complexo proteína-ligante, Protein-ligand complex, Binding affinity prediction model, Empiric nonparametric predictive model, Plataforma Sting, Binding properties, Models,
Online Access:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1060954
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id	dig-alice-doc-1060954
record_format	koha
spelling	dig-alice-doc-10609542017-08-16T04:03:59Z Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions. BORRO, L. YANO, I. H. MAZONI, I. NESHICH, G. LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA. Interações entre proteína e ligantes Modelagem Modelos Complexo proteína-ligante Protein-ligand complex Binding affinity prediction model Empiric nonparametric predictive model Plataforma Sting Binding properties Models 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. 3Dsig 2016. Pôster #56. 2017-01-17T11:11:11Z 2017-01-17T11:11:11Z 2017-01-17 2016 2020-01-21T11:11:11Z Anais e Proceedings de eventos In: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s.n.], 2016. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1060954 en eng openAccess 1 pôster. p. 116-117.
institution	EMBRAPA
collection	DSpace
country	Brasil
countrycode	BR
component	Bibliográfico
access	En linea
databasecode	dig-alice
tag	biblioteca
region	America del Sur
libraryname	Sistema de bibliotecas de EMBRAPA
language	English eng
topic	Interações entre proteína e ligantes Modelagem Modelos Complexo proteína-ligante Protein-ligand complex Binding affinity prediction model Empiric nonparametric predictive model Plataforma Sting Binding properties Models Interações entre proteína e ligantes Modelagem Modelos Complexo proteína-ligante Protein-ligand complex Binding affinity prediction model Empiric nonparametric predictive model Plataforma Sting Binding properties Models
spellingShingle	Interações entre proteína e ligantes Modelagem Modelos Complexo proteína-ligante Protein-ligand complex Binding affinity prediction model Empiric nonparametric predictive model Plataforma Sting Binding properties Models Interações entre proteína e ligantes Modelagem Modelos Complexo proteína-ligante Protein-ligand complex Binding affinity prediction model Empiric nonparametric predictive model Plataforma Sting Binding properties Models BORRO, L. YANO, I. H. MAZONI, I. NESHICH, G. Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
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.
author2	LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA.
author_facet	LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA. BORRO, L. YANO, I. H. MAZONI, I. NESHICH, G.
format	Anais e Proceedings de eventos
topic_facet	Interações entre proteína e ligantes Modelagem Modelos Complexo proteína-ligante Protein-ligand complex Binding affinity prediction model Empiric nonparametric predictive model Plataforma Sting Binding properties Models
author	BORRO, L. YANO, I. H. MAZONI, I. NESHICH, G.
author_sort	BORRO, L.
title	Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
title_short	Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
title_full	Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
title_fullStr	Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
title_full_unstemmed	Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
title_sort	binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
publishDate	2017-01-17
url	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1060954
work_keys_str_mv	AT borrol bindingaffinitypredictionusinganonparametricregressionmodelbasedonphysicochemicalandstructuraldescriptorsofthenanoenvironmentforproteinligandinteractions AT yanoih bindingaffinitypredictionusinganonparametricregressionmodelbasedonphysicochemicalandstructuraldescriptorsofthenanoenvironmentforproteinligandinteractions AT mazonii bindingaffinitypredictionusinganonparametricregressionmodelbasedonphysicochemicalandstructuraldescriptorsofthenanoenvironmentforproteinligandinteractions AT neshichg bindingaffinitypredictionusinganonparametricregressionmodelbasedonphysicochemicalandstructuraldescriptorsofthenanoenvironmentforproteinligandinteractions
_version_	1756023048739225600

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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