School of Medicine, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
Graduate Program in Biochemical Sciences, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
J Chem Inf Model. 2024 Mar 25;64(6):1984-1995. doi: 10.1021/acs.jcim.3c01439. Epub 2024 Mar 12.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (M) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. M is a target for anti-SARS-CoV-2 drug development, and multiple M crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an M consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 M inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential M inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on M enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC values in the midmicromolar range. The M consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against M. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.
严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)主要蛋白酶(M)是一种酶,可切割从病毒基因组翻译的病毒多蛋白,对病毒复制至关重要。M 是抗 SARS-CoV-2 药物开发的靶点,已有多种与竞争性抑制剂结合的 M 晶体被报道。在这项研究中,我们旨在开发 M 共识药效团作为扩展抑制剂搜索的工具。我们通过对齐和总结来自 SARS-CoV-2 M 抑制剂的 152 个生物活性构象的药效团点,生成了一个共识模型。针对亚组配体的构象库进行验证表明,我们的模型在 77%的情况下检索到了重现晶体结合模式的构象。使用源自共识药效团的模型,我们筛选了超过 3.4 亿种化合物。药效团匹配和化学信息学分析确定了新的潜在 M 抑制剂。候选化合物与参考集在化学上不同,其中一些化合物证明了我们模型的相关性。我们评估了 16 种候选化合物对 M 酶活性的影响,发现其中 7 种具有抑制活性。三种化合物(1、4 和 5)的 IC 值在中微摩尔范围内。本文报道的 M 共识药效团可用于识别对 M 具有改善活性和新型化学骨架的化合物。提供了用于生成共识药效团的方法作为开放访问代码(https://github.com/AngelRuizMoreno/ConcensusPharmacophore),并可应用于其他药理靶点。