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Xiaowen Tang

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Open access Aug 2026

Unveiling the allosteric inhibition mechanism of SARS-CoV-2 main protease and discovery of a novel allosteric inhibitor.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) is a crucial therapeutic target for anti-coronavirus disease 2019 (COVID-19) drug development, as it is essential for viral replication. However, mutations within the active site have compromised the efficacy of current competitive inhibitors, prompting the exploration of alternative inhibition strategies. In this study, we systematically investigated the allosteric inhibition mechanism of SARS-CoV-2 Mpro by pelitinib and leveraged this insight for new inhibitor discovery. Through extensive molecular dynamics simulations, we showed that pelitinib exerts allosteric inhibition via the L141-S144-C145-H41 interaction network: it restricts the flexibility of L141 through CH-π interactions, transmits this effect to C145 via S144, stabilizes the hydrogen bond between C145 and H41, and thereby reduces the flexibility of the S3 helix (residues 40-60). This series of conformational changes induces the contraction of the Mpro catalytic pocket from ~1200 ų to ~800 ų, impairs substrate binding, and ultimately appears to impair Mpro activity. Based on this mechanism, we performed structure-based virtual screening and identified a novel compound (Cpd-1). Biological evaluations showed that Cpd-1 exhibits superior Mpro inhibitory activity compared to pelitinib, with negligible off-target binding to human EGFR and Myt1 kinase, low cytotoxicity (cell viability > 60% at 200 μM), and predicted inhibitory activity against clinically relevant Mpro-resistant mutants based on computational analysis. Our findings provide mechanistic insights into a key allosteric mechanism for Mpro inhibition but also provide a promising chemical scaffold for further development as an Mpro-targeting inhibitor.

Quanling Zhang, Tingting Wen, Meng-Si Li et al. · 0 citations
Open access Jun 2026

Quantitative Prediction of the Transformation Potential of Polyfluoroalkyl Substances: A Computational and Machine Learning Study of •OH‑Initiated Initial Reaction Kinetics

This work establishes a robust predictive framework for the •OH-initiated initial transformation potential of polyfluoroalkyl substances, providing a high-throughput tool for the environmental risk assessment and preliminary screening of polyfluoroalkyl alternatives with controlled transformation behavior.

Jinyang Li, Xinying Lan, Jianying Liu et al. · 0 citations