IQUIBICEN 23947
INSTITUTO DE QUIMICA BIOLOGICA DE LA FACULTAD DE CIENCIAS EXACTAS Y NATURALES
Unidad Ejecutora - UE
artículos
Título:
AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions
Autor/es:
AVENDAÑO, DEMIAN; AMBROSIO, FRANCESCA ALESSANDRA; MARTI, MARCELO A; AVENDAÑO, DEMIAN; AMBROSIO, FRANCESCA ALESSANDRA; MARTI, MARCELO A; ARCON, JUAN PABLO; LOPEZ, ELIAS D; TURJANSKI, ADRIAN G; ARCON, JUAN PABLO; LOPEZ, ELIAS D; TURJANSKI, ADRIAN G; MODENUTTI, CARLOS P; DEFELIPE, LUCAS A; FORLI, STEFANO; MODENUTTI, CARLOS P; DEFELIPE, LUCAS A; FORLI, STEFANO
Revista:
BIOINFORMATICS (OXFORD, ENGLAND)
Editorial:
OXFORD UNIV PRESS
Referencias:
Año: 2019
ISSN:
1367-4803
Resumen:
The performance of docking calculations can be improved by tuning parameters for thesystem of interest, e.g., biasing the results towards the formation of relevant protein-ligandinteractions, such as known ligand pharmacophore or interaction sites derived from cosolventmolecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method thatallows the introduction of different types of user-defined biases for fine-tuning AutoDock4 dockingcalculations.
