Pojtanadithee Piyatida, Isswanich Kulpornsorn, Buaban Koonchira, Chamni Supakarn, Wilasluck Patcharin, Deetanya Peerapon, Wangkanont Kittikhun, Langer Thierry, Wolschann Peter, Sanachai Kamonpan, Rungrotmongkol Thanyada
Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand.
Pharmaceutical Sciences and Technology Program, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok 10330, Thailand; Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok 10330, Thailand; Natural Products and Nanoparticles Research Unit (NP2), Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok 10330, Thailand.
Biophys Chem. 2023 Oct 20;304:107125. doi: 10.1016/j.bpc.2023.107125.
Drug development requires significant time and resources, and computer-aided drug discovery techniques that integrate chemical and biological spaces offer valuable tools for the process. This study focused on the field of COVID-19 therapeutics and aimed to identify new active non-covalent inhibitors for 3CL, a key protein target. By combining in silico and in vitro approaches, an in-house database was utilized to identify potential inhibitors. The drug-likeness criteria were considered to pre-filter 553 compounds from 12 groups of natural products. Using structure-based virtual screening, 296 compounds were identified that matched the chemical features of SARS-CoV-2 3CL peptidomimetic inhibitor pharmacophore models. Subsequent molecular docking resulted in 43 hits with high binding affinities. Among the hits, caffeic acid analogs showed significant interactions with the 3CL active site, indicating their potential as promising candidates. To further evaluate their efficacy, enzyme-based assays were conducted, revealing that two ester derivatives of caffeic acid (4k and 4l) exhibited more than a 30% reduction in 3CL activity. Overall, these findings suggest that the screening approach employed in this study holds promise for the discovery of novel anti-SARS-CoV-2 therapeutics. Furthermore, the methodology could be extended for optimization or retrospective evaluation to enhance molecular targeting and antiviral efficacy of potential drug candidates.
药物研发需要大量的时间和资源，而整合化学和生物空间的计算机辅助药物发现技术为这一过程提供了有价值的工具。本研究聚焦于新冠病毒治疗领域，旨在识别针对关键蛋白靶点3CL的新型活性非共价抑制剂。通过结合计算机模拟和体外实验方法，利用一个内部数据库来识别潜在的抑制剂。考虑药物相似性标准对来自12组天然产物的553种化合物进行预筛选。使用基于结构的虚拟筛选，识别出296种与严重急性呼吸综合征冠状病毒2 3CL拟肽抑制剂药效团模型化学特征相匹配的化合物。随后的分子对接产生了43个具有高结合亲和力的命中物。在这些命中物中，咖啡酸类似物与3CL活性位点显示出显著的相互作用，表明它们作为有前景的候选物的潜力。为了进一步评估它们的功效，进行了基于酶的测定，结果显示咖啡酸的两种酯衍生物（4k和4l）使3CL活性降低了30%以上。总体而言，这些发现表明本研究采用的筛选方法在发现新型抗严重急性呼吸综合征冠状病毒2治疗药物方面具有前景。此外，该方法可扩展用于优化或回顾性评估，以增强潜在药物候选物的分子靶向性和抗病毒功效。
