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<p>To establish a procedure for screening compounds that inhibit ligand–receptor binding, we employed a multidimensional virtual-system coupled molecular dynamics (mD-VcMD), a generalized ensemble method recently developed by our group. In this approach, each compound is initially placed far from the receptor. Both receptor and compound were fully flexible in explicit solvent during sampling. The mD-VcMD generated a free-energy landscape of the compound–receptor interactions, in which a probability of existence was assigned to each sampled conformation. We examined four compounds that bind to the papain-like protease (PLpro) of SARS-CoV-2. The resultant free-energy landscapes were funnel-like for all compounds. The probabilities assigned to the free-energy basins correlated well with the measured dissociation constants. Furthermore, structural clustering revealed two types of binding modes within the free-energy basin. The probabilities assigned to the binding modes correlated well with the measured enzyme inhibitory activity. These results suggest that the proposed procedure is effective for selecting candidate inhibitors among the examined compounds.</p>

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Affinity of drug candidates binding to SARS-CoV-2 PLpro assessed using a generalized-ensemble method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月11日

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  • Masashi Muramoto,
  • Simon Hikiri,
  • Suzuka Saito,
  • Qilin Xie,
  • Kota Kasahara,
  • Junichi Higo,
  • Takuya Takahashi

摘要

To establish a procedure for screening compounds that inhibit ligand–receptor binding, we employed a multidimensional virtual-system coupled molecular dynamics (mD-VcMD), a generalized ensemble method recently developed by our group. In this approach, each compound is initially placed far from the receptor. Both receptor and compound were fully flexible in explicit solvent during sampling. The mD-VcMD generated a free-energy landscape of the compound–receptor interactions, in which a probability of existence was assigned to each sampled conformation. We examined four compounds that bind to the papain-like protease (PLpro) of SARS-CoV-2. The resultant free-energy landscapes were funnel-like for all compounds. The probabilities assigned to the free-energy basins correlated well with the measured dissociation constants. Furthermore, structural clustering revealed two types of binding modes within the free-energy basin. The probabilities assigned to the binding modes correlated well with the measured enzyme inhibitory activity. These results suggest that the proposed procedure is effective for selecting candidate inhibitors among the examined compounds.