Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, Medyczna 9, 30-688, Kraków, Poland.
Department of Pharmaceutical Chemistry, Phillips-University, Marbacher Weg 6, 35037, Marburg, Germany.
J Comput Aided Mol Des. 2020 Jun;34(6):697-707. doi: 10.1007/s10822-020-00301-5. Epub 2020 Feb 28.
Among still comparatively few G protein-coupled receptors, the adenosine A 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 A receptor (AAR) 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 AAR and the AAR 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 AAR 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.
在数量相对较少的 G 蛋白偶联受体中，已对几种配体（激动剂和拮抗剂）与腺苷 A 受体进行了共结晶。因此，它可以作为一个模板，具有描述良好的腺苷受体的正位配体结合区域。由于尚未对所有亚型进行结晶，并且为了研究同源模型在这种情况下的可用性，之前已经获得了多种腺苷 A 受体 (AAR) 同源模型，并对接了一系列类先导化合物。结果，确定了一些针对预期靶标的有效且选择性的配体。然而，在体外实验验证研究中，许多配体也与 AAR 和 AAR 亚型结合。在这项工作中，我们提出了一个问题，即根据对接评分是否可以对配体进行分类。因此，我们构建了一个 AAR 同源模型，并将之前发现的所有配体对接至所有三种受体亚型。作为一种度量标准，我们采用了基于出租车几何形状的体外/计算选择排名系统，并获得了具有合理分离度的分类模型。在下一步中，该方法通过具有类似良好性能的外部选择性配体库进行了验证。该分类系统在进一步的筛选中也可能有用。
