Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19

Author:

Li Zhe1ORCID,Wu Chengkun2ORCID,Li Yishui2,Liu Runduo1ORCID,Lu Kai2,Wang Ruibo2,Liu Jie2,Gong Chunye2,Yang Canqun3,Wang Xin45,Zhan Chang-Guo67,Luo Hai-Bin18

Affiliation:

1. School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, P. R. China

2. State Key Laboratory of High Performance Computing, Changsha, P. R. China

3. National Supercomputer Center in Tianjin, Tianjin, P. R. China

4. School of Medicine and Pharmacy, Ocean University of China, Qingdao, P. R. China

5. Marine Biomedical Research Institute of Qingdao, Qingdao, P. R. China

6. Molecular Modeling and Biopharmaceutical Center, College of Pharmacy, University of Kentucky, Lexington, KY, USA

7. Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, KY, USA

8. School of Pharmaceutical Sciences, Hainan University, Haikou, P. R. China

Abstract

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 M pro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards M pro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

Funder

National Key R&D Program of China

Fundamental Research Funds for Hainan University

the special scientific research fund for COVID-19 from the Pilot National Laboratory for Marine Science and Technology

Natural Science Foundation of Guangzhou City

open fund from the State Key Laboratory of High Performance Computing

Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar Funded Scheme

National Natural Science Foundation of China

Taishan Scholars Program

National Science Foundation

Innovative Leader of Qingdao Program

Publisher

SAGE Publications

Subject

Hardware and Architecture,Theoretical Computer Science,Software

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