Accelerating drug discovery for Disease X <i>via</i> an AlphaFold2 driven drug repositioning strategy
With global concerns about possible outbreaks of Disease X similar to COVID-19 in the future, it is crucial to develop and refine effective drug discovery strategies. Here, we reported a drug discovery strategy for Disease X combined with the FDA-approved drug database, AlphaFold (AF) predicted structures, and virtual screening of integrated our developed molecular docking method (MDCC). A case study of revisiting the main protease (M<sup>pro</sup>) of SARS-CoV-2 demonstrated that the lead drug can be discovered through this strategy. First, the M<sup>pro</sup>-peptidyl substrate complex structure was predicted using AF2 based on the amino acid sequences of M<sup>pro</sup> and its peptidyl substrate. Then, the AF2 predicted structure was optimized through molecular dynamics simulations employing a peptidyl substrate-induced binding pocket approach. Binding free energy analysis revealed that the optimized M<sup>pro</sup> exhibited enhanced affinity for the peptidyl substrate, with a Δ<i>G</i><sub>bind</sub> of -103.8 kcal mol<sup>-1</sup>. Subsequently, this optimized complex structure was used to perform MDCC-based virtual screening of 2005 FDA-approved drugs, from which six drug candidates were selected for M<sup>pro</sup> activity determination. Ultimately, Goserelin was identified as a lead compound against M<sup>pro</sup>, demonstrating a 75% inhibition rate. Further IC<sub>50</sub> determination yielded values of 3.79 μM (pH = 7.5) and 2.05 μM (pH = 6.6), comparable to several reported noncovalent M<sup>pro</sup> inhibitors (X77, MCULE-5948770040, and ML188). This work provided a feasible drug discovery strategy in response to the Disease X.
Authors
- Kebin Liu (ORCID: https://orcid.org/0000-0003-1965-7240)
- Xueping Hu (ORCID: https://orcid.org/0000-0002-3014-6719)
- Jiayi Zhao (ORCID: https://orcid.org/0000-0001-9975-9911)
- Wei Deng (ORCID: https://orcid.org/0000-0002-3671-5951)
- Wentao Qi (ORCID: https://orcid.org/0000-0002-2726-9494)
- Huijian Zhao
Institutions
- Shandong University (CN)
- Linyi University (CN)
- Institut des Sciences Moléculaires (FR)
Publication Details
- Journal
- Physical Chemistry Chemical Physics
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5cp01365h
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00