Olosunde Adedapo, Hu Xiche
Department of Chemistry and Biochemistry, University of Toledo, Toledo, OH 43606, USA.
Molecules. 2025 May 15;30(10):2174. doi: 10.3390/molecules30102174.
The SARS-CoV-2 main protease (Mpro), essential for viral replication, remains a prime target for antiviral drug design against COVID-19 and related coronaviruses. In this study, we present a systematic investigation into the molecular determinants of Mpro inhibition using an integrated approach combining large-scale data mining, cheminformatics, and quantum chemical calculations. A curated dataset comprising 963 high-resolution structures of Mpro-ligand complexes-348 covalent and 615 non-covalent inhibitors-was mined from the Protein Data Bank. Cheminformatics analysis revealed distinct physicochemical profiles for each inhibitor class: covalent inhibitors tend to exhibit higher hydrogen bonding capacity and sp character, while non-covalent inhibitors are enriched in aromatic rings and exhibit greater aromaticity and lipophilicity. A novel descriptor, Weighted Hydrogen Bond Count (WHBC), normalized for molecular size, revealed a notable inverse correlation with aromatic ring count, suggesting a compensatory relationship between hydrogen bonding and π-mediated interactions. To elucidate the energetic underpinnings of molecular recognition, 40 representative inhibitors (20 covalent, 20 non-covalent) were selected based on principal component analysis and aromatic ring content. Quantum mechanical calculations at the double-hybrid B2PLYP/def2-QZVP level quantified non-bonded interaction energies, revealing that covalent inhibitors derive binding strength primarily through hydrogen bonding (63.8%), whereas non-covalent inhibitors depend predominantly on π-π stacking and CH-π interactions (62.8%). Representative binding pocket analyses further substantiate these findings: the covalent inhibitor F2F-2020198-00X exhibited strong hydrogen bonds with residues such as Glu166 and His163, while the non-covalent inhibitor EDG-MED-10fcb19e-1 engaged in extensive π-mediated interactions with residues like His41, Met49, and Met165. The distinct interaction patterns led to the establishment of pharmacophore models, highlighting key recognition motifs for both covalent and non-covalent inhibitors. Our findings underscore the critical role of aromaticity and non-bonded π interactions in driving binding affinity, complementing or, in some cases, substituting for hydrogen bonding, and offer a robust framework for the rational design of next-generation Mpro inhibitors with improved selectivity and resistance profiles.
严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）主要蛋白酶（Mpro）对于病毒复制至关重要，仍然是针对COVID-19及相关冠状病毒进行抗病毒药物设计的主要靶点。在本研究中，我们采用大规模数据挖掘、化学信息学和量子化学计算相结合的综合方法，对Mpro抑制的分子决定因素进行了系统研究。从蛋白质数据库中挖掘出一个经过整理的数据集，其中包括963个Mpro-配体复合物的高分辨率结构——348个共价抑制剂和615个非共价抑制剂。化学信息学分析揭示了每种抑制剂类别的不同物理化学特征：共价抑制剂往往表现出更高的氢键结合能力和sp特征，而非共价抑制剂富含芳香环，表现出更大的芳香性和亲脂性。一种针对分子大小进行归一化的新型描述符——加权氢键计数（WHBC），显示出与芳香环计数显著的负相关，表明氢键和π介导的相互作用之间存在补偿关系。为了阐明分子识别的能量基础，基于主成分分析和芳香环含量选择了40种代表性抑制剂（20种共价抑制剂，20种非共价抑制剂）。在双杂化B2PLYP/def2-QZVP水平上的量子力学计算量化了非键相互作用能，结果表明共价抑制剂主要通过氢键（约63.8%）获得结合强度，而非共价抑制剂主要依赖于π-π堆积和CH-π相互作用（约62.8%）。代表性结合口袋分析进一步证实了这些发现：共价抑制剂F2F-2020198-00X与Glu166和His163等残基形成了强氢键，而非共价抑制剂EDG-MED-10fcb19e-1与His41、Met49和Met165等残基发生了广泛的π介导相互作用。这些不同的相互作用模式导致了药效团模型的建立，突出了共价和非共价抑制剂的关键识别基序。我们的研究结果强调了芳香性和非键π相互作用在驱动结合亲和力方面的关键作用，在某些情况下补充或替代了氢键，并为合理设计具有改进的选择性和抗性特征的下一代Mpro抑制剂提供了一个强大的框架。
