Inan Tugce, Yuce Merve, MacKerell Alexander D, Kurkcuoglu Ozge
Department of Chemical Engineering, Istanbul Technical University, Istanbul 34469, Turkey.
Chemical Engineering Department, Faculty of Engineering & Architecture, Istanbul Beykent University, Istanbul 34396, Turkey.
ACS Omega. 2024 Sep 13;9(38):40154-40171. doi: 10.1021/acsomega.4c06172. eCollection 2024 Sep 24.
G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators.
G蛋白偶联受体（GPCRs）在细胞信号传导中起着核心作用，并且与许多疾病相关。因此，需要计算方法来探索这类蛋白质的潜在变构位点，以促进潜在调节剂的识别。重要的是，丰富的结构数据提供了靶向不同GPCRs的正构配体和变构调节剂的位置，这使得验证识别新变构结合位点的方法成为可能。在此，我们验证了两种计算技术的组合，即残基相互作用网络（RIN）模型和配体竞争饱和法识别位点（SILCS）方法，以预测A类GPCRs的假定变构结合位点。RIN分析可识别介导受体变构信号传导且在配体结合后具有改变受体动力学高能力的枢纽残基。通过包含105个晶体结构（91个配体结合型、14个未结合型）的数据集，RIN成功预测了18种不同A类GPCRs的已知正构（和变构）结合位点，基于实验和理论结合位点数据，灵敏度高达77.8%（76.9%），特异性为92.5%（95.3%），精确率为51.9%（50%），准确率为86.2%（92.4%）。此外，对残基网络的图谱谱分析表明，所提出的位点位于高度互连的残基簇的界面处，具有协调功能动力学的高能力。然后，我们采用SILCS-热点方法评估了为7种对多种疾病至关重要的不同A类GPCRs预测的新位点的成药可能性。虽然我们的方法成功地探索了已知的正构和变构结合位点，但也提出了许多具有结合类药物分子潜力的假定变构位点。本文提出的计算方法有望成为预测GPCRs假定变构位点以促进有效调节剂设计的高效工具。
