Panwar Umesh, Singh Sanjeev Kumar
Computer Aided Drug Design and Molecular Modelling Laboratory, Department of Bioinformatics, Alagappa University, Karaikudi-630 004, Tamil Nadu, India.
Endocr Metab Immune Disord Drug Targets. 2019;19(4):449-457. doi: 10.2174/1871530319666181128100903.
Obesity is well known multifactorial disorder towards the public health concern in front of the world. Increasing rates of obesity are characterized by liver diseases, chronic diseases, diabetes mellitus, hypertension and stroke, improper function of the heart, reproductive and gastrointestinal diseases, and gallstones. An essential enzyme pancreatic lipase recognized for the digestion and absorption of lipids can be a promising drug target towards the future development of antiobesity therapeutics in the cure of obesity disorders.
The purpose of present study is to identify an effective potential therapeutic agent for the inhibition of pancreatic lipase.
A trio of in-silico procedure of HTVS, SP and XP in Glide module, Schrodinger with default parameters, was applied on Specs databases to identify the best potential compound based on receptor grid. Finally, based on binding interaction, docking score and glide energy, selected compounds were taken forward to the platform of IFD, ADME, MMGBSA, DFT, and MDS for analyzing the ligands behavior into the protein binding site.
Using in silico protocol of structure-based virtual screening on pancreatic lipase top two compounds AN-465/43369242 & AN-465/43384139 from Specs database were reported. The result suggested that both the compounds are competitive inhibitors with higher docking score and greatest binding affinity than the reported inhibitor.
We anticipate that results could be future therapeutic agents and may present an idea toward the experimental studies against the inhibition of pancreatic lipase.
肥胖是一种众所周知的多因素疾病,是全球公共卫生关注的焦点。肥胖率上升的特征表现为肝脏疾病、慢性疾病、糖尿病、高血压和中风、心脏功能异常、生殖和胃肠道疾病以及胆结石。一种参与脂质消化和吸收的关键酶——胰脂肪酶,有望成为未来治疗肥胖症的抗肥胖治疗药物靶点。
本研究旨在确定一种有效的潜在治疗药物来抑制胰脂肪酶。
在Schrodinger软件的Glide模块中,采用具有默认参数的高通量虚拟筛选(HTVS)、标准精度(SP)和额外精度(XP)三种计算机模拟程序,对Specs数据库进行分析,以基于受体网格确定最佳潜在化合物。最后,根据结合相互作用、对接分数和Glide能量,将选定的化合物提交到诱导契合对接(IFD)、药物代谢及药物动力学(ADME)、分子力学广义Born表面面积(MMGBSA)、密度泛函理论(DFT)和分子动力学模拟(MDS)平台,分析配体在蛋白质结合位点的行为。
通过对胰脂肪酶进行基于结构的虚拟筛选计算机模拟方案,从Specs数据库中筛选出了两种排名靠前的化合物AN - 465/43369242和AN - 465/43384139。结果表明,这两种化合物都是竞争性抑制剂,其对接分数和结合亲和力均高于已报道的抑制剂。
我们预期这些结果可能成为未来的治疗药物,并可能为针对胰脂肪酶抑制的实验研究提供思路。