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首页> 外文期刊>Open Pharmaceutical Sciences Journal >Structural Insights into the Molecular Design of HER2 Inhibitors
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Structural Insights into the Molecular Design of HER2 Inhibitors

机译：HER2抑制剂分子设计的结构见解

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Background:The present study was aimed at designing some potential candidates as HER2 inhibitors used in breast cancer.Methods:An energy optimized pharmacophore (E-pharmacophore) model was developed and used to screen the molecular databases (such as ASINEX and NCI databases) against a six site (ADHRRR) hypothesis. The shape similarity of the retrieved hits was calculated and then filtered applying ADME and Lipinski’s filters. Further, these hits were docked into the crystal structure of HER2 protein (3W32) using Glide XP protocol to obtain the docking poses and XP gscores. The performance of the virtual screening (VS) methods was evaluated using Schr?dinger’s decoy set of 1000 molecules. Ranking of the actives in the VS protocol was assessed by a variety of well-established methods including the average rank of actives, EF, ROC, BEDROC, AUAC, and the RIE. The retrieved hits were submitted to Canvas for generating binary fingerprints (dendritic) to identify structural diversity among the hits and clustered on the basis of Tanimoto coefficient using hierarchical clustering.Results:Seven structurally diverse clusters were selected applying above protocol, having XP gscores >-10, and fitness scores > 1, considering top scoring cluster representative from each cluster. The best scoring hit 355682-ASINEX was submitted to Combiglide to discover some better candidates with improved scores. Finally, structural interaction fingerprint (SIFT) analysis was employed to study the binding interaction, which showed H-bond interaction with Met793, Gln791 and Thr854 residues of HER2 protein.Conclusion:The applied methodology and the retrieved hits could be useful in the design of potent inhibitors of HER2 proteins, commonly found to be expressed in the breast cancer patients.
机译：背景：本研究旨在设计一些潜在的候选药物作为乳腺癌中的HER2抑制剂。方法：建立了能量优化的药效团（E-pharmacophore）模型并用于筛选针对该药的分子数据库（如ASINEX和NCI数据库）六点假说（ADHRRR）。计算检索到的匹配数据的形状相似度，然后使用ADME和Lipinski的过滤器进行过滤。此外，使用Glide XP协议将这些命中点停靠到HER2蛋白（3W32）的晶体结构中，以获得停靠姿势和XP gscores。虚拟筛选（VS）方法的性能是使用Schr？dinger的1000个分子诱饵集评估的。 VS协议中的活性成分等级通过多种公认的方法进行评估，包括活性成分，EF，ROC，BERDRC，AUAC和RIE的平均等级。将检索到的命中结果提交给Canvas，以生成二叉指纹（树突状），以识别命中物之间的结构多样性，并使用层次聚类在Tanimoto系数的基础上进行聚类。结果：使用上述协议选择了七个结构不同的聚类，其XP gscores> 10，健身得分> 1，请考虑每个组中得分最高的组代表。得分最高的命中率355682-ASINEX已提交给Combiglide，以发现分数更高的更好候选人。最后，通过结构相互作用指纹图谱（SIFT）分析研究了结合相互作用，表明与HER2蛋白的Met793，Gln791和Thr854残基的H键相互作用。 HER2蛋白的有效抑制剂，通常在乳腺癌患者中表达。

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