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

By Yang Liu, Maximilian Grimm, Wen-tao Dai, Mu-chun Hou, Zhixiong Xiao, Yang Cao. Acta Pharmacologica Sinica, 2019

733 citations recorded by the source corpus.

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/.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/f15f8c65825d40cf3e5be4a2208a6f8d392890b5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Xiaocong Yang
2. Anima Anandkumar
3. Weili Nie
4. Shuang Chen
5. Zhixiong Xiao
6. Yang Cao
7. Yang Liu
8. Arash Vahdat

In Molecular Biology

1. Xiaocong Yang
2. Anima Anandkumar
3. Weili Nie
4. Shuang Chen
5. Zhixiong Xiao
6. Yang Cao
7. Yang Liu
8. Arash Vahdat

In Pharmacology

1. Anima Anandkumar
2. Weili Nie
3. Arash Vahdat
4. Thomas F. Miller
5. Zhuoran Qiao

Citations in the corpus

• CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting (2022, methodology): “It employed our protein-surface-curvature-based cavity detection approach (CurPocket) (13,14) to guide the molecular docking with AutoDock Vina (version 1.1.2) (15,16).”
• Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022, methodology): “Results are compared to a recent learning-based method EquiBind [43]; for reference, we also include results from a physics-based blind docking method CB-Dock [44] obtained with ground-truth all-atom receptor inputs and using a computing budget similar to learning-based methods.”