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Discovery of novel inhibitors for c-Met by virtual screening and pharmacophore analysis

机译:通过虚拟筛选和药效团分析发现新的c-Met抑制剂

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The overexpression of human c-Met tyrosine kinase induces the tumor proliferation and migration. Commercial drug is still not available for inhibiting this target. The structure of human c-Met was simulated and validated by molecular modeling. Compounds from our laboratory database, including natural products and anticancer agents, were employed for the docking analysis. De Novo drug design was further performed for three compounds with highest DockScore value to discover the novel inhibitors. Through the adsorption, distribution, metabolism, excretion and toxicity (ADMET) descriptor, only compounds A(2), B-3, C-2 and C-5 were selected. Through the pharmacophore analysis. AGR1086, HIS1088, AGR1208, ASN1209, ALA1226. ARG1227, ASP1228, TYR1230, and GLU1233 were suggested as the key residues because of strong pharmacophore features. In addition, the pharmacophore features of candidates consisted with the active site properties. The added fragments produced the strong interaction with the surrounding residues and yielded hydrogen bonds (HBs). Thus, the interaction energy between the ligand and the receptor was enhanced. Besides, the values of several scoring functions (PLP1, PLP2, and DockScore) of candicates were comparatively higher than compounds A. B. C. and Kirin. According to the aforementioned analyses, compounds A, A(2), B, B-3, C, C-2 and C-5 were suggested as the potent c-Met inhibitors. Besides, the scaffolds of compounds A, B, and C provided the direction for further drug design. (C) 2008 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
机译:人c-Met酪氨酸激酶的过表达诱导肿瘤增殖和迁移。商业药物仍然不能用于抑制该靶标。通过分子建模对人c-Met的结构进行了模拟和验证。对接分析采用了我们实验室数据库中的化合物,包括天然产物和抗癌剂。对三种具有最高DockScore值的化合物进一步进行了De Novo药物设计,以发现新型抑制剂。通过吸附,分布,代谢,排泄和毒性(ADMET)描述符,仅选择化合物A(2),B-3,C-2和C-5。通过药效团分析。 AGR1086,HIS1088,AGR1208,ASN1209,ALA1226。由于强大的药效基团功能,建议将ARG1227,ASP1228,TYR1230和GLU1233作为关键残基。另外,候选物的药效基团特征由活性位点性质组成。添加的片段与周围的残基产生强烈的相互作用,并产生氢键(HBs)。因此,增强了配体与受体之间的相互作用能。此外,候选词的几种得分函数(PLP1,PLP2和DockScore)的值相对高于化合物A. B. C.和Kirin。根据上述分析,建议化合物A,A(2),B,B-3,C,C-2和C-5作为有效的c-Met抑制剂。此外,化合物A,B和C的支架为进一步药物设计提供了指导。 (C)2008台湾化学工程师学会。由Elsevier B.V.发布。保留所有权利。

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