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A cross docking pipeline for improving pose prediction and virtual screening performance

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Abstract

Pose prediction and virtual screening performance of a molecular docking method depend on the choice of protein structures used for docking. Multiple structures for a target protein are often used to take into account the receptor flexibility and problems associated with a single receptor structure. However, the use of multiple receptor structures is computationally expensive when docking a large library of small molecules. Here, we propose a new cross-docking pipeline suitable to dock a large library of molecules while taking advantage of multiple target protein structures. Our method involves the selection of a suitable receptor for each ligand in a screening library utilizing ligand 3D shape similarity with crystallographic ligands. We have prospectively evaluated our method in D3R Grand Challenge 2 and demonstrated that our cross-docking pipeline can achieve similar or better performance than using either single or multiple-receptor structures. Moreover, our method displayed not only decent pose prediction performance but also better virtual screening performance over several other methods.

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Acknowledgements

We acknowledge RIKEN ACCC for the supercomputing resources at the Hokusai GreatWave supercomputer used in this study. We acknowledge RIKEN Pioneering Project in Dynamic Structural Biology for funding. We thank members of our lab for help and discussions.

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  1. Structural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan

    Ashutosh Kumar & Kam Y. J. Zhang

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  1. Ashutosh Kumar
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Correspondence to Kam Y. J. Zhang.

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Kumar, A., Zhang, K.Y.J. A cross docking pipeline for improving pose prediction and virtual screening performance. J Comput Aided Mol Des 32, 163–173 (2018). https://doi.org/10.1007/s10822-017-0048-z

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