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  • Title: Predicting protein-ligand binding modes for CELPP and GC3: workflows and insight.
    Author: Xu X, Ma Z, Duan R, Zou X.
    Journal: J Comput Aided Mol Des; 2019 Mar; 33(3):367-374. PubMed ID: 30689079.
    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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