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A Taxicab geometry quantification system to evaluate the performance of in silico methods: a case study on adenosine receptors ligands.
Journal of Computer-Aided Molecular Design ( IF 3.0 ) Pub Date : 2020-02-28 , DOI: 10.1007/s10822-020-00301-5
Kamil J Kuder 1 , Ilona Michalik 1 , Katarzyna Kieć-Kononowicz 1 , Peter Kolb 2
Affiliation  

Among still comparatively few G protein-coupled receptors, the adenosine A2A receptor has been co-crystallized with several ligands, agonists as well as antagonists. It can thus serve as a template with a well-described orthosteric ligand binding region for adenosine receptors. As not all subtypes have been crystallized yet, and in order to investigate the usability of homology models in this context, multiple adenosine A1 receptor (A1AR) homology models had been previously obtained and a library of lead-like compounds had been docked. As a result, a number of potent and one selective ligand toward the intended target have been identified. However, in in vitro experimental verification studies, many ligands also bound to the A2AAR and the A3AR subtypes. In this work we asked the question whether a classification of the ligands according to their selectivity was possible based on docking scores. Therefore, we built an A3AR homology model and docked all previously found ligands to all three receptor subtypes. As a metric, we employed an in vitro/in silico selectivity ranking system based on taxicab geometry and obtained a classification model with reasonable separation. In the next step, the method was validated with an external library of, selective ligands with similarly good performance. This classification system might also be useful in further screens.

中文翻译:

用于评估计算机方法性能的出租车几何量化系统:腺苷受体配体的案例研究。

在仍然相对较少的 G 蛋白偶联受体中,腺苷 A2A 受体已与几种配体、激动剂和拮抗剂共结晶。因此,它可以用作具有充分描述的腺苷受体正构配体结合区的模板。由于尚未确定所有亚型,为了研究同源模型在这种情况下的可用性,先前已获得多个腺苷 A1 受体 (A1AR) 同源模型,并对接了一个类铅化合物库。因此,已鉴定出针对预期靶标的多种有效配体和一种选择性配体。然而,在体外实验验证研究中,许多配体也与 A2AAR 和 A3AR 亚型结合。在这项工作中,我们提出了一个问题,是否可以基于对接分数根据配体的选择性对配体进行分类。因此,我们建立了一个 A3AR 同源模型,并将所有先前发现的配体与所有三种受体亚型对接。作为衡量标准,我们采用了基于出租车几何形状的体外/计算机选择性排序系统,并获得了具有合理分离度的分类模型。在下一步中,该方法使用具有类似良好性能的选择性配体的外部库进行验证。这个分类系统在进一步的筛选中也可能有用。我们采用了基于出租车几何形状的体外/计算机选择性排序系统,并获得了具有合理分离度的分类模型。在下一步中,该方法使用具有类似良好性能的选择性配体的外部库进行验证。这个分类系统在进一步的筛选中也可能有用。我们采用了基于出租车几何形状的体外/计算机选择性排序系统,并获得了具有合理分离度的分类模型。在下一步中,该方法使用具有类似良好性能的选择性配体的外部库进行验证。这个分类系统在进一步的筛选中也可能有用。
更新日期:2020-02-28
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