Benchmarking reverse docking through AlphaFold2 human proteome

Author:

Luo Qing1,Wang Sheng2,Li Hoi Yeung3,Zheng Liangzhen45,Mu Yuguang3,Guo Jingjing1ORCID

Affiliation:

1. Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences Macao Polytechnic University Macao China

2. Shanghai Zelixir Biotech Company Ltd. China

3. School of Biological Sciences Nanyang Technological University Singapore

4. Shenzhen Zelixir Biotech Company Ltd. China

5. Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Abstract

AbstractPredicting the binding of ligands to the human proteome via reverse‐docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off‐target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet‐SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off‐target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.

Funder

National Key Research and Development Program of China

Publisher

Wiley

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