Discovery of Human HMG-Coa Reductase Inhibitors Using Structure-Based Pharmacophore Modeling Combined with Molecular Dynamics Simulation Methodologies

Minky Son, Chanin Park, Ayoung Baek, Shalini John, Keun Woo Lee
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Abstract

3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) catalyzes the conversion of HMG-CoA to mevalonate using NADPH and the enzyme is involved in rate-controlling step of mevalonate. Inhibition of HMGR is considered as effective way to lower cholesterol levels so it is drug target to treat hypercholesterolemia, major risk factor of cardiovascular disease. To discover novel HMGR inhibitor, we performed structure-based pharmacophore modeling combined with molecular dynamics (MD) simulation. Four HMGR inhibitors were used for MD simulation and representative structure of each simulation were selected by clustering analysis. Four structure-based pharmacophore models were generated using the representative structure. The generated models were validated used in virtual screening to find novel scaffolds for inhibiting HMGR. The screened compounds were filtered by applying drug-like properties and used in molecular docking. Finally, four hit compounds were obtained and these complexes were refined using energy minimization. These compounds might be potential leads to design novel HMGR inhibitor. Keywords—Anti-hypercholesterolemia drug, HMGR inhibitor, Molecular dynamics simulation, Structure-based pharmacophore modeling.
利用基于结构的药效团模型结合分子动力学模拟方法发现人类HMG-Coa还原酶抑制剂
3-羟基-3-甲基戊二酰辅酶A还原酶(HMGR)利用NADPH催化HMG-CoA转化为甲羟戊二酸,并参与甲羟戊二酸的速率控制步骤。抑制HMGR被认为是降低胆固醇水平的有效途径,是治疗心血管疾病主要危险因素高胆固醇血症的药物靶点。为了发现新的HMGR抑制剂,我们将基于结构的药效团模型与分子动力学(MD)模拟相结合。采用4种HMGR抑制剂进行MD模拟,通过聚类分析选择每种模拟的代表性结构。利用代表性结构生成了4个基于结构的药效团模型。生成的模型在虚拟筛选中得到验证,用于寻找抑制HMGR的新型支架。利用类药物性质对筛选的化合物进行筛选,并将其应用于分子对接。最后得到了四种合适的配合物,并利用能量最小化法对这些配合物进行了细化。这些化合物可能是设计新型HMGR抑制剂的潜在线索。关键词:抗高胆固醇血症药物,HMGR抑制剂,分子动力学模拟,基于结构的药效团模型
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