Saad Ali Alshehri, Shadma Wahab, Mohammad Ali Abdullah Almoyad
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The initial compounds were screened through various tests, including analysis of their physical and chemical properties, PAINS filter, ADMET analysis, PASS analysis, and specific interaction analysis. We selected those that showed high binding affinity and specificity to PKCα from the screened compounds, and we further analyzed them using molecular dynamics simulations (MDS) and principal component analysis (PCA). Various systematic parameters from the MDS analyses suggested that the protein-ligand complexes were stabilized throughout the simulation trajectories of 100 nanoseconds (ns). Our findings indicated that compounds Nicandrenone and Withaphysalin D bind to PKCα with high stability and affinity, making them potential candidates for further research in cancer therapeutics innovation in clinical contexts.Communicated by Ramaswamy H. Sarma.</p>","PeriodicalId":15272,"journal":{"name":"Journal of Biomolecular Structure & Dynamics","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"<i>In silico</i> identification of potential protein kinase C alpha inhibitors from phytochemicals from IMPPAT database for anticancer therapeutics: a virtual screening approach.\",\"authors\":\"Saad Ali Alshehri, Shadma Wahab, Mohammad Ali Abdullah Almoyad\",\"doi\":\"10.1080/07391102.2023.2252086\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Protein Kinase C alpha (PKCα) is a critical signaling molecule that plays a crucial role in various physiological processes, including cell growth, differentiation, and survival. 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引用次数: 0
摘要
蛋白激酶Cα (PKCα)是一种重要的信号分子,在包括细胞生长、分化和存活在内的各种生理过程中起着至关重要的作用。多年来,人们对靶向PKCα作为治疗包括癌症在内的各种疾病的有希望的药物靶点越来越感兴趣。因此,靶向PKCα可以作为一种潜在的策略来预防癌症的进展,并提高传统抗癌疗法的疗效。我们利用IMPPAT数据库中的天然化合物对具有抗癌潜力的靶向PKCα的化合物进行了系统的搜索。通过理化性质分析、PAINS筛选、ADMET分析、PASS分析、特异相互作用分析等测试筛选初始化合物。我们从筛选的化合物中筛选出对PKCα具有高结合亲和力和特异性的化合物,并利用分子动力学模拟(MDS)和主成分分析(PCA)对其进行进一步分析。MDS分析的各种系统参数表明,在100纳秒(ns)的模拟轨迹中,蛋白质-配体复合物是稳定的。我们的研究结果表明,Nicandrenone和Withaphysalin D与PKCα结合具有高度的稳定性和亲和力,这使它们成为癌症治疗创新临床研究的潜在候选者。由Ramaswamy H. Sarma传达。
In silico identification of potential protein kinase C alpha inhibitors from phytochemicals from IMPPAT database for anticancer therapeutics: a virtual screening approach.
Protein Kinase C alpha (PKCα) is a critical signaling molecule that plays a crucial role in various physiological processes, including cell growth, differentiation, and survival. Over the years, there has been a growing interest in targeting PKCα as a promising drug target for the treatment of various diseases, including cancer. Targeting PKCα can, therefore, serve as a potential strategy to prevent cancer progression and enhance the efficacy of conventional anticancer therapies. We conducted a systematic search for promising compounds for their anticancer potential that target PKCα using natural compounds from the IMPPAT database. The initial compounds were screened through various tests, including analysis of their physical and chemical properties, PAINS filter, ADMET analysis, PASS analysis, and specific interaction analysis. We selected those that showed high binding affinity and specificity to PKCα from the screened compounds, and we further analyzed them using molecular dynamics simulations (MDS) and principal component analysis (PCA). Various systematic parameters from the MDS analyses suggested that the protein-ligand complexes were stabilized throughout the simulation trajectories of 100 nanoseconds (ns). Our findings indicated that compounds Nicandrenone and Withaphysalin D bind to PKCα with high stability and affinity, making them potential candidates for further research in cancer therapeutics innovation in clinical contexts.Communicated by Ramaswamy H. Sarma.
期刊介绍:
The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.