Fayaz S M, Rajanikant G K
School of Biotechnology, National Institute of Technology Calicut, Calicut, 673601, India.
J Comput Aided Mol Des. 2014 Jul;28(7):779-94. doi: 10.1007/s10822-014-9771-x. Epub 2014 Jul 1.
Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.
尽管该领域正在进行大量研究,但程序性细胞死亡仍是一个引人入胜的研究领域,因为它带来了新的挑战和问题。最近,坏死性凋亡作为一种程序性坏死细胞死亡形式,已被认为与包括神经疾病在内的多种疾病有关。受体相互作用丝氨酸/苏氨酸蛋白激酶1(RIPK1)是参与坏死性凋亡的一种重要调节蛋白,抑制该蛋白对于阻止坏死性凋亡过程并最终阻止细胞死亡至关重要。当前基于结构的虚拟筛选方法涉及广泛的策略,最近,强调考虑多种蛋白质结构以提取药效团作为改善结果的一种方法。然而,在对接过程中完全利用药效团信息非常重要。此外,在这类方法中,使用合适的蛋白质结构进行对接是可取的。否则,通过基于药效团的筛选获得的潜在化合物命中物在对接后可能没有正确的排名和分数。因此,全面整合不同的集成方法至关重要,这可能会提供更好的虚拟筛选结果。在本研究中,采用了一种新型计算策略——双集成筛选来鉴定针对RIPK1的多样且有效的抑制剂。利用无配体和有配体蛋白质结构捕获结合位点中存在的所有药效团特征,并通过组合这些特征构建一个集成药效团。该集成药效团用于基于药效团的ZINC数据库筛选。由此获得的化合物命中物进行集成对接。通过对接获得的先导化合物通过特征评估和分子动力学模拟进一步验证。