In silico prediction of SARS protease inhibitors by virtual high throughput screening.
A structure-based in silico virtual drug discovery procedure was assessed with severe acute respiratory syndrome coronavirus main protease serving as a case study. First, potential compounds were extracted from protein-ligand complexes selected from Protein Data Bank database based on structural similarity to the target protein. Later, the set of compounds was ranked by docking scores using a Electronic High-Throughput Screening flexible docking procedure to select the most promising molecules. The set of best performing compounds was then used for similarity search over the 1 million entries in the Ligand.Info Meta-Database. Selected molecules having close structural relationship to a 2-methyl-2,4-pentanediol may provide candidate lead compounds toward the development of novel allosteric severe acute respiratory syndrome protease inhibitors.
Plewczynski
Dariusz
D
Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Pawinskiego 5a Street, 02-106 Warsaw, Poland. d.plewczynski@icm.edu.pl
Hoffmann
Marcin
M
von Grotthuss
Marcin
M
Ginalski
Krzysztof
K
Rychewski
Leszek
L
D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D010450:Endopeptidases / Q000378:metabolism*; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation; D011480:Protease Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D045473:Severe acute respiratory syndrome-related coronavirus / Q000187:drug effects* / Q000201:enzymology*; D013329:Structure-Activity Relationship
D008024:Ligands; D011480:Protease Inhibitors; D010450:Endopeptidases