Article

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

Authors: Yang Liu1, Maximilian Grimm1, Wen-tao Dai2, Mu-chun Hou1, Zhi-Xiong Xiao1, Yang Cao1
1 Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
2 Shanghai Center for Bioinformation Technology & Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology Institute, Shanghai 201203, China
Correspondence to: Yang Cao: cao@scu.edu.cn,
DOI: 10.1038/s41401-019-0228-6
Received: 5 December 2018
Accepted: 14 March 2019
Advance online: 1 July 2019

Abstract

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/.
Keywords: bioinformatics; computer-aided design; computer-aided drug discovery

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