Abstract
BackgroundCross-docking is an approach to find the best holo structures among multiple structures available for a target protein.ResultsCrossDocker significantly decreases the time needed for setting parameters and inputs for performing multiple dockings, data collection and subsequent analysis.ConclusionCrossDocker was written in Python language and is available as executable binary for Windows operating system. It is available at http://www.pharm-sbg.com. Some example data sets were also provided.
Highlights
Cross-docking is an approach to find the best holo structures among multiple structures available for a target protein
In this paper we described CrossDocker which can significantly accelerate performing multiple dockings, data collection and subsequent analysis
The RMSDs of self-dockings are in bold faces xlsx” generated by CrossDocker from hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase data set
Summary
Cross-docking is an approach to find the best holo structures among multiple structures available for a target protein. In case of multiple available 3D structures for a receptor, selection of the best structure for pose prediction and virtual screening is an important issue (Mohan et al 2005). It was suggested that the structures that have the best ability to dock non-native ligands with lower RMSD with reference to the crystalled pose of the ligands are probably more successful in prediction of binding pose (Zhang et al 2014) of the ligands correctly and virtual screenings (Ramezani and Shamsara 2015).
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