GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches.

However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces.

Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces.

All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

近年来，GPCR寡聚已成为GPCR领域的热门话题。尽管尚未完全了解这些相互作用的工作原理，但这些低聚物中的一部分受体会彼此影响。GPCR之间如此高度复杂的相互作用网络的存在为新的治疗方法提供了替代靶标的可能性。

但是，在表征这些复合物时仍然存在挑战，尤其是在界面一级。通常将不同的实验方法（例如FRET或BRET）组合起来研究GPCR寡聚物之间的相互作用。计算方法已被用作从GPCR序列和少数X射线可分辨的寡聚结构检索信息的有用工具，以及预测新的和可信赖的GPCR寡聚界面。

机器学习（ML）方法最近在其他方法的一些障碍中有所帮助。通过在同一算法上加入和评估多个基于结构，序列和协同进化的特征，可以将实验方法产生的特定结构和残基问题稀释为能够准确预测GPCR的全能算法-GPCR接口。

所有这些作为单一方法或组合方法使用的方法都提供了有关GPCR寡聚化及其在GPCR功能和动力学中的作用的有用信息。总之，我们介绍了用于研究低聚物界面的实验，计算和机器学习方法，以及用于靶向这些动态复合物的策略。