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

  • Zhe Li
  • , Chengkun Wu
  • , Yishui Li
  • , Runduo Liu
  • , Kai Lu
  • , Ruibo Wang
  • , Jie Liu
  • , Chunye Gong
  • , Canqun Yang
  • , Xin Wang
  • , Chang Guo Zhan
  • , Hai Bin Luo

Producción científica: Articlerevisión exhaustiva

14 Citas (Scopus)

Resumen

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 Mpro 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 Mpro, 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.

Idioma originalEnglish
Páginas (desde-hasta)45-57
Número de páginas13
PublicaciónInternational Journal of High Performance Computing Applications
Volumen37
N.º1
DOI
EstadoPublished - ene 2023

Nota bibliográfica

Publisher Copyright:
© The Author(s) 2022.

Financiación

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We cordially acknowledge National Key R&D Program of China (2017YFB0202600), National Natural Science Foundation of China (81903542, 21877134, 22077143, and U1811462), Fundamental Research Funds for Hainan University (KYQD (ZR)-21,031), Science Foundation of Guangzhou City (202102021151, 201904020023), Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar Funded Scheme (2016), the National Science Foundation (NSF, grant CHE-1111761), the Taishan Scholars Program (tsqn201909170), the Innovative Leader of Qingdao Program (19-3-2–26-zhc), the special scientific research fund for COVID-19 from the Pilot National Laboratory for Marine Science and Technology (QNLM202001), and open fund from the State Key Laboratory of High Performance Computing (201901–11).

FinanciadoresNúmero del financiador
Fundamental Research Funds for Hainan University
Innovative Leader of Qingdao Program19-3-2–26-zhc
KYQD
State Key Laboratory of High Performance Computing201901–11
Taishan Scholars Programtsqn201909170
National Science Foundation Arctic Social Science ProgramCHE-1111761
National Science Foundation Arctic Social Science Program
Natural Science Foundation of Guangzhou City201904020023, 202102021151
Natural Science Foundation of Guangzhou City
National Natural Science Foundation of China (NSFC)81903542, 21877134, U1811462, 22077143
National Natural Science Foundation of China (NSFC)
National Key Basic Research and Development Program of China2017YFB0202600
National Key Basic Research and Development Program of China
Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme
Polit National Laboratory for Marine Science and TechnologyQNLM202001
Polit National Laboratory for Marine Science and Technology

    ASJC Scopus subject areas

    • Software
    • Theoretical Computer Science
    • Hardware and Architecture

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