人工智能促进了新型 COVID-19 S-RBD 域抑制剂的虚拟筛选、基于结构的命中优化和合成。

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-11-13 DOI:10.1021/acs.jcim.4c01110
Ioannis Gkekas, Sotirios Katsamakas, Stelios Mylonas, Theano Fotopoulou, George Ε Magoulas, Alia Cristina Tenchiu, Marios Dimitriou, Apostolos Axenopoulos, Nafsika Rossopoulou, Simona Kostova, Erich E Wanker, Theodora Katsila, Demetris Papahatjis, Vassilis G Gorgoulis, Maria Koufaki, Ioannis Karakasiliotis, Theodora Calogeropoulou, Petros Daras, Spyros Petrakis
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引用次数: 0

摘要

冠状病毒病 2019(COVID-19)是由一种新型高致病性严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)引起的,它通过跨膜尖峰(S)糖蛋白感染人体细胞。S 蛋白的受体结合域(RBD)与宿主细胞的血管紧张素转换酶 II(ACE2)受体相互作用。因此,针对这种相互作用的药理作用可能会阻止病毒的感染或传播。在此,我们进行了一次虚拟筛选,以确定能阻断 S-ACE2 相互作用的小分子。我们根据ADME-Tox描述符对大型化合物库进行了筛选,以确定其药物样特性、杂合性和蛋白-蛋白相互作用靶向能力,同时排除泛检测干扰化合物。对剩余的化合物(约占起始数据集的 10%)采用了适当设计的基于人工智能的虚拟筛选流水线,搜索能与 S 蛋白的 RBD 结合的分子。根据筛选得分对所有分子进行排序,根据其结构进行分组,并对其与 ACE2 受体可能的相互作用模式进行后过滤,最终得出 31 个命中分子。我们进一步测试了这些命中分子对 Spike RBD/ACE2 (19-615) 相互作用的抑制作用。六种化合物以剂量依赖的方式抑制了 S-ACE2 相互作用,其中两种化合物还阻止了由 SARS-CoV-2 的 S 蛋白介导的伪型病毒对人体细胞的感染。在这两种化合物中,苯并咪唑衍生物 CKP-22 也能保护 Vero E6 细胞免受 SARS-CoV-2 感染。随后,通过合成 29 种新的衍生物对 CKP-22 进行了 "一击即中 "的优化,其中化合物 CKP-25 抑制了 Spike RBD/ACE2 (19-615) 的相互作用,降低了 SARS-CoV-2 在 Vero E6 细胞中的细胞病理效应(IC50 = 3.5 μM),并减少了细胞培养上清液中的病毒载量。早期的体外 ADME-Tox 研究表明,CKP-25 不存在生物降解或肝脏代谢问题,而 CYP450 同工酶特异性实验表明,CKP-25 是 CYP450 系统的弱抑制剂。此外,CKP-25 对大肠杆菌 WP2 uvrA 菌株没有诱变作用。因此,CKP-25 被认为是抗 COVID-19 感染的先导化合物。
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AI Promoted Virtual Screening, Structure-Based Hit Optimization, and Synthesis of Novel COVID-19 S-RBD Domain Inhibitors.

Coronavirus disease 2019 (COVID-19) is caused by a new, highly pathogenic severe-acute-respiratory syndrome coronavirus 2 (SARS-CoV-2) that infects human cells through its transmembrane spike (S) glycoprotein. The receptor-binding domain (RBD) of the S protein interacts with the angiotensin-converting enzyme II (ACE2) receptor of the host cells. Therefore, pharmacological targeting of this interaction might prevent infection or spread of the virus. Here, we performed a virtual screening to identify small molecules that block S-ACE2 interaction. Large compound libraries were filtered for drug-like properties, promiscuity and protein-protein interaction-targeting ability based on their ADME-Tox descriptors and also to exclude pan-assay interfering compounds. A properly designed AI-based virtual screening pipeline was applied to the remaining compounds, comprising approximately 10% of the starting data sets, searching for molecules that could bind to the RBD of the S protein. All molecules were sorted according to their screening score, grouped based on their structure and postfiltered for possible interaction patterns with the ACE2 receptor, yielding 31 hits. These hit molecules were further tested for their inhibitory effect on Spike RBD/ACE2 (19-615) interaction. Six compounds inhibited the S-ACE2 interaction in a dose-dependent manner while two of them also prevented infection of human cells from a pseudotyped virus whose entry is mediated by the S protein of SARS-CoV-2. Of the two compounds, the benzimidazole derivative CKP-22 protected Vero E6 cells from infection with SARS-CoV-2, as well. Subsequent, hit-to-lead optimization of CKP-22 was effected through the synthesis of 29 new derivatives of which compound CKP-25 suppressed the Spike RBD/ACE2 (19-615) interaction, reduced the cytopathic effect of SARS-CoV-2 in Vero E6 cells (IC50 = 3.5 μM) and reduced the viral load in cell culture supernatants. Early in vitro ADME-Tox studies showed that CKP-25 does not possess biodegradation or liver metabolism issues, while isozyme-specific CYP450 experiments revealed that CKP-25 was a weak inhibitor of the CYP450 system. Moreover, CKP-25 does not elicit mutagenic effect on Escherichia coli WP2 uvrA strain. Thus, CKP-25 is considered a lead compound against COVID-19 infection.

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来源期刊
CiteScore
9.80
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10.70%
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529
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1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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