CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting

By Yang Liu, Xiaocong Yang, Jianhong Gan, Shuang Chen, Zhixiong Xiao, Yang Cao. Nucleic Acids Res., 2022

441 citations recorded by the source corpus.

Abstract Protein-ligand blind docking is a powerful method for exploring the binding sites of receptors and the corresponding binding poses of ligands. It has seen wide applications in pharmaceutical and biological researches. Previously, we proposed a blind docking server, CB-Dock, which has been under heavy use (over 200 submissions per day) by researchers worldwide since 2019. Here, we substantially improved the docking method by combining CB-Dock with our template-based docking engine to enhance the accuracy in binding site identification and binding pose prediction. In the benchmark tests, it yielded the success rate of ∼85% for binding pose prediction (RMSD < 2.0 Å), which outperformed original CB-Dock and most popular blind docking tools. This updated docking server, named CB-Dock2, reconfigured the input and output web interfaces, together with a highly automatic docking pipeline, making it a particularly efficient and easy-to-use tool for the bioinformatics and cheminformatics communities. The web server is freely available at https://cadd.labshare.cn/cb-dock2/.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d5146f64831181c8a1c186a5625312bc4a4784e4