Title
Assessing an Ensemble Docking-Based Virtual Screening Strategy for Kinase Targets by Considering Protein Flexibility.
Abstract
In this study, to accommodate receptor flexibility, based on multiple receptor conformations, a novel ensemble docking protocol was developed by using the naive Bayesian classification technique, and it was evaluated in terms of the prediction accuracy of docking-based virtual screening (VS) of three important targets in the kinase family: ALK, CDK2, and VEGFR2. First, for each target, the representative crystal structures were selected by structural clustering, and the capability of molecular docking based on each representative structure to discriminate inhibitors from non-inhibitors was examined. Then, for each target, 50 ns molecular dynamics (MD) simulations were carried out to generate an ensemble of the conformations, and multiple representative structures/snapshots were extracted from each MD trajectory by structural clustering. On average, the representative crystal structures outperform the representative structures extracted from MD simulations in terms of the capabilities to separate inhibitors from non-inhibitors. Finally, by using the naive Bayesian classification technique, an integrated VS strategy was developed to combine the prediction results of molecular docking based on different representative conformations chosen from crystal structures and MD trajectories. It was encouraging to observe that the integrated VS strategy yields better performance than the docking-based VS based on any single rigid conformation. This novel protocol may provide an improvement over existing strategies to search for more diverse and promising active compounds for a target of interest.
Year
DOI
Venue
2014
10.1021/ci500414b
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Field
DocType
Volume
Docking (molecular),VEGF receptors,Naive Bayes classifier,Docking (dog),Chemistry,Molecular dynamics,Bioinformatics,Cluster analysis,Virtual screening,Snapshot (computer storage)
Journal
54
Issue
ISSN
Citations 
10
1549-9596
9
PageRank 
References 
Authors
0.59
17
7
Name
Order
Citations
PageRank
Sheng Tian1333.67
Huiyong Sun2346.09
Peichen Pan324322.23
Dan Li4266.82
Xuechu Zhen591.26
Youyong Li623818.54
Tingjun Hou742754.50