CB-Dock: a web server for cavity detection-guided protein–ligand blind docking


As the number of elucidated protein structures is rapidly increasing, the growing data call for methods to efficiently exploit the structural information for biological and pharmaceutical purposes. Given the three-dimensional (3D) structure of a protein and a ligand, predicting their binding sites and affinity are a key task for computer-aided drug discovery. To address this task, a variety of docking tools have been developed. Most of them focus on docking in the preset binding sites given by users. To automatically predict binding modes without information about binding sites, we developed a user-friendly blind docking web server, named CB-Dock, which predicts binding sites of a given protein and calculates the centers and sizes with a novel curvature-based cavity detection approach, and performs docking with a popular docking program, Autodock Vina. This method was carefully optimized and achieved ~70% success rate for the top-ranking poses whose root mean square deviation (RMSD) were within 2 Å from the X-ray pose, which outperformed the state-of-the-art blind docking tools in our benchmark tests. CB-Dock offers an interactive 3D visualization of results, and is freely available at http://cao.labshare.cn/cb-dock/.

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The authors thank Professor Jian-yi Yang of Nankai University for helping in running COACH-D and Dr. Holger Stitz of Johannes Kepler University Linz for his invaluable editing of the manuscript. We also thank Professor Yang Zhang and Cheng-xin Zhang of the University of Michigan, Professor Xiang-jun Du of Sun Yat-sen University and Dr. Zhi-chao Miao of Cambridge University for invaluable discussions. This work was supported by the National Natural Science Foundation of China (Grant numbers 31401130, 81830108, and 81672736), the National Key R&D Program of China (2018YFC0910500), the Shanghai Sailing Program (16YF1408600), the funding for prevention and control technology of African swine fever (2018NZ0151) and the Shanghai Industrial Technology Institute (17CXXF008).

Author information

YL designed and optimized the CB-Dock tool and wrote the manuscript. MG built the CB-Dock web server. WTD benchmarked the program. MCH tested the server. ZXX guided the experiments. YC designed the project and wrote the manuscript.

Correspondence to Yang Cao.

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  • bioinformatics
  • computer-aided design
  • computer-aided drug discovery