GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery.

中文翻译：

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GPCR热稳定的计算设计。

GPCR超家族是人类基因组中最大的临床相关靶标家族；然而，众所周知，这些完整的膜蛋白的低热稳定性和高构象可塑性使其在生化，生物物理和结构实验中难以操作。在这里，我们描述了计算方法的最新进展，这些方法为GPCR设计稳定化突变利用了受体的结构和序列保守性，并利用机器学习对超家族的累积突变数据进行了研究。快速有效的计算工具可以为现有的实验突变筛选提供可行的替代方法，并有望通过扩展用于训练机器学习模型的热稳定性数据集而进一步改进。