Artículo
AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions
Arcon, Juan Pablo
; Modenutti, Carlos Pablo
; Avendaño, Demian; Lopez, Elias Daniel
; Defelipe, Lucas Alfredo
; Ambrosio, Francesca Alessandra; Turjanski, Adrian
; Forli, Stefano; Marti, Marcelo Adrian
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Fecha de publicación:
10/2019
Editorial:
Oxford University Press
Revista:
Bioinformatics (Oxford, England)
ISSN:
1367-4803
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Summary: The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. Availability and implementation: AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. Supplementary information: Supplementary data are available at Bioinformatics online.
Palabras clave:
bioinformatica
,
docking
,
drug design
,
autodock
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Articulos(IQUIBICEN)
Articulos de INSTITUTO DE QUIMICA BIOLOGICA DE LA FACULTAD DE CS. EXACTAS Y NATURALES
Articulos de INSTITUTO DE QUIMICA BIOLOGICA DE LA FACULTAD DE CS. EXACTAS Y NATURALES
Citación
Arcon, Juan Pablo; Modenutti, Carlos Pablo; Avendaño, Demian; Lopez, Elias Daniel; Defelipe, Lucas Alfredo; et al.; AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions; Oxford University Press; Bioinformatics (Oxford, England); 35; 19; 10-2019; 3836-3838
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