AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions
Title | AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Arcon, Juan Pablo, Modenutti Carlos P., Avendaño Demian, Lopez Elias D., Defelipe Lucas A., Ambrosio Francesca Alessandra, Turjanski Adrian G., Forli Stefano, and Martí Marcelo A. |
Journal | Bioinformatics |
Volume | 35 |
Issue | 19 |
Pagination | 3836 - 3838 |
Date Published | 2019 |
ISBN Number | 1367-4803 |
Abstract | 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.AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at https://ccsb.scripps.edu/mgltools/ or https://autodockbias.wordpress.com.Supplementary data are available at Bioinformatics online. |
URL | https://doi.org/10.1093/bioinformatics/btz152 |
Short Title | Bioinformatics |
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