基于配体的虚拟筛选发现SARS-CoV-2主要蛋白酶的潜在抑制剂。

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Gurmeet Kaur, Bhupesh Goyal
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引用次数: 0

摘要

主要蛋白酶(Mpro,也称为3CLpro)是严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)的关键酶,由于其在病毒复制和转录中的关键作用,一直被认为是药物开发的主要目标。重要的是,超过1300万个SARS-CoV-2序列的高度保守性使Mpro成为抗病毒治疗的一个有希望的靶点,以阻止SARS-CoV-2的遗传进化。在这项工作中,使用基于配体的虚拟筛选(LBVS)从各种小分子数据库中筛选约1600万种化合物,以boceprevir作为参比化合物,以鉴定新的Mpro小分子抑制剂。Boceprevir[丙型肝炎病毒(HCV)药物]已被重新用作抗Mpro活性的候选药物(IC50 = 4.13±0.61 μM)。利用分子对接技术,从850个化合物文库中鉴定出了结合亲和力(-9.9 ~ -8.0 kcal mol-1)高于boceprevir (-7.5 kcal mol-1)的先导化合物。此外,分子力学泊松-玻尔兹曼表面积(MM-PBSA)分析表明,ChEMBL144205 (C3)、ZINC000091755358 (C5)和ZINC000092066113 (C9)对Mpro的结合亲和度分别为-65.2±6.5、-66.1±7.1和-67.3±5.8 kcal mol-1。鉴定出的化合物在不违反利平斯基规则五的情况下显示出良好的药物相似性。分子动力学(MD)模拟显示,与apo-Mpro和Mpro-boceprevir相比,Mpro与C3、C5和C9结合后具有更高的结构稳定性和更低的残留水平波动。值得注意的是,构象聚类和FEL分析描述了C3与Mpro的Thr26、氧阴离子孔残基(Asn142和Gly143)、催化残基(Cys145)和Glu166的氢键相互作用,表明其具有很强的结合亲和力和潜在的抑制作用。本工作中采用的综合计算方法确定了有希望的抗Mpro活性先导化合物,这需要进一步的实验验证,以开发它们作为抗SARS-CoV-2的抗病毒药物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ligand-based virtual screening to discover potential inhibitors of SARS-CoV-2 main protease.

The main protease (Mpro, also known as 3CLpro), a pivotal enzyme of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been considered a prime target for drug development due to its crucial role in viral replication and transcription. Importantly, a high degree of conservation in more than 13 million SARS-CoV-2 sequences affords Mpro as a promising target for antiviral therapy to impede the genetic evolution of SARS-CoV-2. In this work, ∼16 million compounds from various small molecule databases were screened using ligand-based virtual screening (LBVS) with boceprevir as the reference compound to identify new small molecule inhibitors of Mpro. Boceprevir [hepatitis C virus (HCV) drug] has been repurposed as a drug candidate against Mpro activity (IC50 = 4.13 ± 0.61 μM). The lead compounds exhibiting higher binding affinities (-9.9 to -8.0 kcal mol-1) than boceprevir (-7.5 kcal mol-1) were identified from a library of 850 compounds using molecular docking. Furthermore, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis depicted ChEMBL144205 (C3), ZINC000091755358 (C5), and ZINC000092066113 (C9) with binding affinities of -65.2 ± 6.5, -66.1 ± 7.1, and -67.3 ± 5.8 kcal mol-1, respectively, as high-affinity binders to Mpro. The identified compounds displayed a favourable drug-likeness profile without violating Lipinski's rule of five. Molecular dynamics (MD) simulations revealed the higher structural stability and reduced residue-level fluctuations in Mpro upon binding of C3, C5, and C9 as compared to apo-Mpro and Mpro-boceprevir. Notably, conformational clustering and FEL analyses depicted hydrogen bond interactions of C3 with Thr26, oxyanion hole residues (Asn142 and Gly143), the catalytic residue (Cys145), and Glu166 of Mpro, suggesting its strong binding affinity and potential inhibitory effect. The integrated computational methodology employed in this work identified promising lead compounds against Mpro activity, which warrants further experimental validation to develop them as antiviral agents against SARS-CoV-2.

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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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