Chiesa Luca, Kellenberger Esther
Laboratoire d'innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, 67400, Illkirch, France.
J Cheminform. 2022 Oct 29;14(1):74. doi: 10.1186/s13321-022-00654-z.
G protein-coupled receptors are involved in many biological processes, relaying the extracellular signal inside the cell. Signaling is regulated by the interactions between receptors and their ligands, it can be stimulated by agonists, or inhibited by antagonists or inverse agonists. The development of a new drug targeting a member of this family requires to take into account the pharmacological profile of the designed ligands in order to elicit the desired response. The structure-based virtual screening of chemical libraries may prioritize a specific class of ligands by combining docking results and ligand binding information provided by crystallographic structures. The performance of the method depends on the relevance of the structural data, in particular the conformation of the targeted site, the binding mode of the reference ligand, and the approach used to compare the interactions formed by the docked ligand with those formed by the reference ligand in the crystallographic structure. Here, we propose a new method based on the conformational dynamics of a single protein-ligand reference complex to improve the biased selection of ligands with specific pharmacological properties in a structure-based virtual screening exercise. Interactions patterns between a reference agonist and the receptor, here exemplified on the β2 adrenergic receptor, were extracted from molecular dynamics simulations of the agonist/receptor complex and encoded in graphs used to train a one-class machine learning classifier. Different conditions were tested: low to high affinity agonists, varying simulation duration, considering or ignoring hydrophobic contacts, and tuning of the classifier parametrization. The best models applied to post-process raw data from retrospective virtual screening obtained by docking of test libraries effectively filtered out irrelevant poses, discarding inactive and non-agonist ligands while identifying agonists. Taken together, our results suggest that consistency of the binding mode during the simulation is a key to the success of the method.
G蛋白偶联受体参与许多生物过程，将细胞外信号传递到细胞内部。信号传导由受体与其配体之间的相互作用调节，可被激动剂刺激，或被拮抗剂或反向激动剂抑制。开发一种靶向该家族成员的新药需要考虑设计配体的药理学特性，以引发所需的反应。基于结构的化学文库虚拟筛选可以通过结合对接结果和晶体结构提供的配体结合信息，对特定类别的配体进行优先排序。该方法的性能取决于结构数据的相关性，特别是靶位点的构象、参考配体的结合模式，以及用于比较对接配体与晶体结构中参考配体形成的相互作用的方法。在此，我们提出一种基于单一蛋白质-配体参考复合物构象动力学的新方法，以改善基于结构的虚拟筛选中具有特定药理学特性的配体的偏向性选择。从激动剂/受体复合物的分子动力学模拟中提取参考激动剂与受体之间的相互作用模式，以β2肾上腺素能受体为例，并编码在用于训练单类机器学习分类器的图中。测试了不同条件：低亲和力到高亲和力激动剂、不同的模拟持续时间、考虑或忽略疏水接触，以及分类器参数化的调整。应用于对测试文库进行对接获得的回顾性虚拟筛选的原始数据后处理的最佳模型有效地滤除了无关的构象，丢弃了无活性和非激动剂配体，同时识别出激动剂。综上所述，我们的结果表明模拟过程中结合模式的一致性是该方法成功的关键。
