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M₂ 毒蕈碱型乙酰胆碱受体亚型的同源建模。

On homology modeling of the M₂ muscarinic acetylcholine receptor subtype.

机构信息

Department of Neurochemistry, Institute of Physiology v.v.i., Academy of Sciences of the Czech Republic, Prague 14200, Czech Republic.

出版信息

J Comput Aided Mol Des. 2013 Jun;27(6):525-38. doi: 10.1007/s10822-013-9660-8. Epub 2013 Jun 28.

DOI:10.1007/s10822-013-9660-8
PMID:23812908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3717152/
Abstract

Twelve homology models of the human M2 muscarinic receptor using different sets of templates have been designed using the Prime program or the modeller program and compared to crystallographic structure (PDB:3UON). The best models were obtained using single template of the closest published structure, the M3 muscarinic receptor (PDB:4DAJ). Adding more (structurally distant) templates led to worse models. Data document a key role of the template in homology modeling. The models differ substantially. The quality checks built into the programs do not correlate with the RMSDs to the crystallographic structure and cannot be used to select the best model. Re-docking of the antagonists present in crystallographic structure and relative binding energy estimation by calculating MM/GBSA in Prime and the binding energy function in YASARA suggested it could be possible to evaluate the quality of the orthosteric binding site based on the prediction of relative binding energies. Although estimation of relative binding energies distinguishes between relatively good and bad models it does not indicate the best one. On the other hand, visual inspection of the models for known features and knowledge-based analysis of the intramolecular interactions allows an experimenter to select overall best models manually.

摘要

已使用 Prime 程序或 modeller 程序设计了 12 个人类 M2 毒蕈碱受体的同源模型,这些模型使用了不同的模板,并与晶体结构(PDB:3UON)进行了比较。使用最接近已发表结构(M3 毒蕈碱受体,PDB:4DAJ)的单个模板获得了最佳模型。添加更多(结构上更远)的模板会导致模型变差。数据表明模板在同源建模中起着关键作用。模型之间存在显著差异。程序中内置的质量检查与晶体结构的 RMSD 不相关,不能用于选择最佳模型。在晶体结构中重新对接拮抗剂,并通过在 Prime 中计算 MM/GBSA 和在 YASARA 中计算结合能函数来估算相对结合能,这表明根据相对结合能的预测,可以评估正位结合位点的质量。尽管相对结合能的估算可以区分相对较好和较差的模型,但它并不能指示最佳模型。另一方面,对模型进行已知特征的视觉检查和基于知识的分子内相互作用分析,允许实验人员手动选择总体最佳模型。

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