The development of reliable ways of predicting the binding free energies of covalent inhibitors is a challenge for computer-aided drug design. Such development is important, for example, in the fight against the SARS-CoV-2 virus, in which covalent inhibitors can provide a promising tool for blocking M^pro, the main protease of the virus. This work develops a reliable and practical protocol for evaluating the binding free energy of covalent inhibitors. Our protocol presents a major advance over other approaches that do not consider the chemical contribution of the binding free energy. Our strategy combines the empirical valence bond method for evaluating the reaction energy profile and the PDLD/S-LRA/β method for evaluating the noncovalent part of the binding process. This protocol has been used in the calculations of the binding free energy of an α-ketoamide inhibitor of M^pro. Encouragingly, our approach reproduces the observed binding free energy. Our study of covalent inhibitors of cysteine proteases indicates that in the choice of an effective warhead it is crucial to focus on the exothermicity of the point on the free energy surface of a peptide cleavage that connects the acylation and deacylation steps. Overall, we believe that our approach should provide a powerful and effective method for in silico design of covalent drugs.

中文翻译：

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探索共价抑制机制：模拟 SARS-CoV-2 主要蛋白酶 α-酮酰胺抑制剂的结合自由能

开发预测共价抑制剂结合自由能的可靠方法是计算机辅助药物设计的一个挑战。这种发展非常重要，例如，在对抗 SARS-CoV-2 病毒时，共价抑制剂可以提供一种有前景的工具来阻断病毒的主要蛋白酶M ^{pro 。}这项工作开发了一种可靠且实用的方案来评估共价抑制剂的结合自由能。我们的协议比其他不考虑结合自由能的化学贡献的方法取得了重大进步。我们的策略结合了用于评估反应能量分布的经验价键方法和用于评估结合过程的非共价部分的 PDLD/S-LRA/β 方法。该协议已用于计算 M ^pro的 α-酮酰胺抑制剂的结合自由能。令人鼓舞的是，我们的方法再现了观察到的结合自由能。我们对半胱氨酸蛋白酶共价抑制剂的研究表明，在选择有效弹头时，关键是要关注连接酰化和脱酰化步骤的肽裂解自由能表面上点的放热性。总的来说，我们相信我们的方法应该为共价药物的计算机设计提供一种强大而有效的方法。