Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh 176215, India.
Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh 176215, India.
Int J Biol Macromol. 2018 Nov;119:352-359. doi: 10.1016/j.ijbiomac.2018.07.050. Epub 2018 Jul 18.
The huge therapeutic potential and the market share of painkillers are well-known. Due to the side effects associated with traditional NSAIDs and selective cyclooxygenase (COX-2) inhibitors, a new generation of painkillers is the need of the hour. In this regard, microsomal prostaglandin E synthase-1 (mPGES-1) offers great potential as an alternative drug target against inflammatory disorders. The present study is aimed at identifying the amino acids crucial in effective inhibitor binding at the mPGES-1 active site by performing molecular dynamics based studies on a series of 7-Phenyl-imidazoquinolin-4(5H)-one derivatives. Molecular dynamics (MD) simulations, MM-PBSA, per-residue energy decomposition and Dimensionality Reduction through Covariance matrix Transformation for Identification of Differences in dynamics (DIRECT-ID) analysis were performed to get insights into the structural details that can aid in novel drug design against mPGES-1. The high correlations of electrostatic and polar energy terms with biological activity highlight their importance and applicability in in silico screening studies. Further, per-residue energy decomposition studies revealed that Lys42, Arg52, Arg122, Pro124, Ser127, Val128 and Thr131 were contributing more towards inhibitor binding energy. The results clearly show that MM-PBSA can act as a filter in virtual screening experiments and can play major role in facilitating various mPGES-1 drug discovery studies.
止痛药具有巨大的治疗潜力和市场份额,这是众所周知的。由于传统非甾体抗炎药和选择性环氧化酶(COX-2)抑制剂的副作用,新一代止痛药是当前的需求。在这方面,微粒体前列腺素 E 合酶-1(mPGES-1)作为一种替代药物靶点,具有治疗炎症性疾病的巨大潜力。本研究旨在通过对一系列 7-苯基-咪唑并喹啉-4(5H)-酮衍生物进行基于分子动力学的研究,确定在 mPGES-1 活性部位有效抑制剂结合的关键氨基酸。进行了分子动力学(MD)模拟、MM-PBSA、每残基能量分解和通过协方差矩阵变换进行维度降低以识别动力学差异(DIRECT-ID)分析,以深入了解有助于针对 mPGES-1 进行新型药物设计的结构细节。静电能和极性能项与生物活性的高度相关性突出了它们在计算机筛选研究中的重要性和适用性。此外,每残基能量分解研究表明,Lys42、Arg52、Arg122、Pro124、Ser127、Val128 和 Thr131 对抑制剂结合能的贡献更大。结果清楚地表明,MM-PBSA 可以作为虚拟筛选实验的过滤器,并在促进各种 mPGES-1 药物发现研究中发挥重要作用。