Computational Biology/Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
Institute of Biology, State University of Campinas, SP, Brazil.
Pharmacol Res. 2021 May;167:105577. doi: 10.1016/j.phrs.2021.105577. Epub 2021 Mar 24.
The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.
最近在寻找直接 KEAP1 抑制剂的呼声中,需要更快、更有效的药物发现过程,这是计算机辅助药物发现(CADD)的固有特性,可以将候选药物推向临床,供患者使用。KEAP1(ARE 主激活剂的负调节剂)作为一种治疗策略,正在出现以对抗氧化应激协调疾病。计算机算法和化合物数据库的进步要求我们强调该技术所具有的功能,可以利用这些功能来针对 KEAP1-Nrf2 PPI。因此,在这篇综述中,我们揭示了在计算机上识别 KEAP1 抑制的方法,根据与相关氨基酸残基的适当拟合,我们发现了另外 3 种和 16 种化合物,它们与 KEAP1 kelch 口袋/结构域完全吻合。我们的目标是利用热点区域进行虚拟片段筛选和鉴定热点残基,利用可以协调 KEAP1 表面可用药性的参数。