Institute for Drug Discovery, Medical Faculty, Leipzig University, 04103 Leipzig, Germany.
Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA.
Int J Mol Sci. 2023 Apr 24;24(9):7788. doi: 10.3390/ijms24097788.
G protein-coupled receptors (GPCRs) are the largest class of drug targets and undergo substantial conformational changes in response to ligand binding. Despite recent progress in GPCR structure determination, static snapshots fail to reflect the conformational space of putative binding pocket geometries to which small molecule ligands can bind. In comparative modeling of GPCRs in the absence of a ligand, often a shrinking of the orthosteric binding pocket is observed. However, the exact prediction of the flexible orthosteric binding site is crucial for adequate structure-based drug discovery. In order to improve ligand docking and guide virtual screening experiments in computer-aided drug discovery, we developed RosettaGPCRPocketSize. The algorithm creates a conformational ensemble of biophysically realistic conformations of the GPCR binding pocket between the TM bundle, which is consistent with a knowledge base of expected pocket geometries. Specifically, tetrahedral volume restraints are defined based on information about critical residues in the orthosteric binding site and their experimentally observed range of C-C-distances. The output of RosettaGPCRPocketSize is an ensemble of binding pocket geometries that are filtered by energy to ensure biophysically probable arrangements, which can be used for docking simulations. In a benchmark set, pocket shrinkage observed in the default RosettaGPCR was reduced by up to 80% and the binding pocket volume range and geometric diversity were increased. Compared to models from four different GPCR homology model databases (RosettaGPCR, GPCR-Tasser, GPCR-SSFE, and GPCRdb), the here-created models showed more accurate volumes of the orthosteric pocket when evaluated with respect to the crystallographic reference structure. Furthermore, RosettaGPCRPocketSize was able to generate an improved realistic pocket distribution. However, while being superior to other homology models, the accuracy of generated model pockets was comparable to AlphaFold2 models. Furthermore, in a docking benchmark using small-molecule ligands with a higher molecular weight between 400 and 700 Da, a higher success rate in creating native-like binding poses was observed. In summary, RosettaGPCRPocketSize can generate GPCR models with realistic orthosteric pocket volumes, which are useful for structure-based drug discovery applications.
G 蛋白偶联受体（GPCRs）是最大的药物靶点类别，它们在响应配体结合时会发生显著的构象变化。尽管在 GPCR 结构测定方面取得了最近的进展，但静态快照无法反映小分子配体可以结合的假定结合口袋几何形状的构象空间。在没有配体的情况下对 GPCR 进行比较建模时，通常会观察到正构结合口袋的缩小。然而，准确预测灵活的正构结合位点对于充分的基于结构的药物发现至关重要。为了改进配体对接并指导计算机辅助药物发现中的虚拟筛选实验，我们开发了 RosettaGPCRPocketSize。该算法创建了一个构象集合，其中包含 TM 束之间的 GPCR 结合口袋的生物物理上合理的构象，这与预期口袋几何形状的知识库一致。具体而言，基于正构结合位点中的关键残基及其观察到的 C-C 距离的实验范围的信息，定义了四面体体积约束。RosettaGPCRPocketSize 的输出是一个结合口袋几何形状的集合，这些集合通过能量进行过滤，以确保具有生物物理可能性的排列，这些排列可用于对接模拟。在基准集中，默认 RosettaGPCR 中观察到的口袋收缩减少了高达 80%，并且结合口袋体积范围和几何多样性增加了。与来自四个不同 GPCR 同源模型数据库（RosettaGPCR、GPCR-Tasser、GPCR-SSFE 和 GPCRdb）的模型相比，根据结晶学参考结构评估时，这里创建的模型显示出更准确的正构口袋体积。此外，RosettaGPCRPocketSize 能够生成改进的现实口袋分布。然而，尽管优于其他同源模型，但生成模型口袋的准确性与 AlphaFold2 模型相当。此外，在使用分子量在 400 至 700 Da 之间的小分子配体进行对接基准测试时，观察到创建天然结合构象的成功率更高。总之，RosettaGPCRPocketSize 可以生成具有现实正构口袋体积的 GPCR 模型，这些模型对于基于结构的药物发现应用很有用。
