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.

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Journal
Physical Chemistry Chemical Physics
Published
2025-01-01
DOI
https://doi.org/10.1039/d5cp01365h
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Accelerating drug discovery for Disease X via an AlphaFold2 driven drug repositioning strategy

Kebin Liu, Xueping Hu, Jiayi Zhao, Wei Deng et al.
Physical Chemistry Chemical Physics
Computational Drug Discovery Methods
article

Accelerating drug discovery for Disease X via an AlphaFold2 driven drug repositioning strategy

Kebin Liu, Xueping Hu, Jiayi Zhao, Wei Deng, Wentao Qi, Huijian Zhao
article en

Abstract

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.

Physical Chemistry Chemical PhysicsVol. 27(42)
Shandong University (CN), Linyi University (CN), Institut des Sciences Moléculaires (FR)
Openalex Percentile: Top 74%
Computational Drug Discovery Methods
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