Alqaaf Muhammad, Nasution Ahmad Kamal, Karim Mohammad Bozlul, Rumman Mahfujul Islam, Sedayu Muhammad Hendrick, Supriyanti Retno, Ono Naoaki, Altaf-Ul-Amin Md, Kanaya Shigehiko
Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.
Department of Electrical Engineering, Jenderal Soedirman University, Purbalingga, 53371, Central Java, Indonesia.
Sci Rep. 2025 Jan 2;15(1):200. doi: 10.1038/s41598-024-83637-4.
The ongoing global pandemic caused by the SARS-CoV-2 virus has demanded the urgent search for effective therapeutic interventions. In response, our research aimed at identifying natural products (NPs) with potential inhibitory effects on the entry of the SARS-CoV-2 spike (S) protein into host cells. Utilizing the Protein Data Bank Japan (PDBJ) and BindingDB databases, we isolated 204 S-glycoprotein sequences and conducted a clustering analysis to identify similarities and differences among them. We subsequently identified 33,722 binding molecules (BMs) by matching them with the sequences of 204 S-glycoproteins and compared them with 52,107 secondary metabolites (SMs) from the KNApSAcK database to identify potential inhibitors. We conducted docking and drug-likeness property analyses to identify several SMs with potential as drug candidates based on binding energy (BE), no Lipinski's rule violation (LV), psychochemical properties within the pink area of the bioavailability radar, and a bioavailability score (BAS) not less than 0.55. Fourteen SMs were predicted through computational analysis as potential candidates for inhibiting the three major types of S proteins. Our study provides a foundation for further experimental validation of these compounds as potential therapeutic agents against SARS-CoV-2.
由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)病毒引发的全球大流行促使人们迫切寻找有效的治疗干预措施。作为回应,我们的研究旨在确定对SARS-CoV-2刺突(S)蛋白进入宿主细胞具有潜在抑制作用的天然产物(NPs)。利用日本蛋白质数据库(PDBJ)和BindingDB数据库,我们分离出204个S糖蛋白序列,并进行聚类分析以确定它们之间的异同。随后,我们通过将它们与204个S糖蛋白的序列进行匹配,鉴定出33722个结合分子(BMs),并将它们与来自KNApSAcK数据库的52107种次生代谢产物(SMs)进行比较,以确定潜在的抑制剂。我们进行了对接和类药性质分析,以根据结合能(BE)、无违反Lipinski规则(LV)、生物利用度雷达粉色区域内的理化性质以及生物利用度评分(BAS)不低于0.55,确定几种具有作为候选药物潜力的SMs。通过计算分析预测了14种SMs作为抑制三种主要类型S蛋白的潜在候选物。我们的研究为进一步实验验证这些化合物作为抗SARS-CoV-2潜在治疗药物奠定了基础。