Abstract
Drug Design Data Resource (D3R) continues to release valuable benchmarking datasets to promote improvement and development of computational methods for new drug discovery. We have developed several methods for protein–ligand binding mode prediction during the participation in the D3R challenges. In the present study, these methods were integrated, automated, and systematically tested using the large-scale data from Continuous Evaluation of Ligand Pose Prediction (CELPP) and a subset of Grand challenge 3 (GC3). The results show that current molecular docking methods benefit from the increasing number of protein–ligand complex structures deposited in Protein Data Bank. Using an appropriate protein structure for docking significantly improves the success rate of the binding mode prediction. The results of our template-based method and docking method are compared and discussed. Our future direction include the combination of these two methods for binding mode prediction.
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Acknowledgements
Support to XZ from OpenEye Scientific Software Inc. (Santa Fe, NM, http://www.eyesopen.com) is gratefully acknowledged. This work was supported by NIH R01GM109980 (PI: XZ), NIH R01HL126774 and NIH R01HL142301 (PI: Cui) to XZ. The computations were performed on the high performance computing infrastructure supported by NSF CNS-1429294 (PI: Chi-Ren Shyu) and the HPC resources supported by the University of Missouri Bioinformatics Consortium (UMBC).
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Xu, X., Ma, Z., Duan, R. et al. Predicting protein–ligand binding modes for CELPP and GC3: workflows and insight. J Comput Aided Mol Des 33, 367–374 (2019). https://doi.org/10.1007/s10822-019-00185-0
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DOI: https://doi.org/10.1007/s10822-019-00185-0