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Decrypting the allosteric network: multi-scale dynamics of SARS-CoV-2 main protease inhibition by Moringa oleifera flavonoids
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DOI：10.1080/07391102.2026.2696960.png)
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Abstract
En 中文
The SARS-CoV-2 main protease (Mpro) is a prototypical enzyme whose activity is governed by long-range allosteric communication within its dynamic structural network. Flavonoids from Moringa oleifera have been reported to interact with Mpro, yet the dynamic mechanisms underlying their modulation remain unclear. Here, we employ an integrated computational biophysics framework to elucidate structure–dynamics–function relationships governing the interaction of 23 natural metabolites with Mpro. Our workflow combines ensemble docking using free-energy-landscape-derived conformations, microsecond-scale aggregate molecular dynamics simulations, and binding energy decomposition to characterize ligand-dependent interaction patterns. By integrating residue interaction network (RIN) analysis and dynamic cross-correlation matrices (DCCM), we demonstrate that specific flavonoids induce pronounced perturbations in a GLU166-centered interaction hub, reshaping long-range correlated motions essential for functional coupling within the protease. These allosteric effects are not captured by conventional affinity-based metrics alone. Explainable machine-learning interpretation and structure–activity relationship analysis further reveal how key physicochemical features modulate dynamic coupling rather than static binding. Collectively, this study provides mechanistic insight into allosteric regulation in Mpro and establishes a generalizable computational framework for probing ligand-induced perturbations of biomolecular dynamic networks.
Keywords:
SARS-CoV-2 Main protease (Mpro)
molecular dynamics simulation
free-energy landscape
ADMET analysis
dynamic cross-correlation matrix
machine learning
residue network analysis
Journal
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2.4
Papers:
827
Citations:
1.5W