Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of membrane proteins. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the PME of transmembrane domain variants of G protein-coupled receptors from structural and/ or evolutionary features. We show that our best-performing network, which we term the PME predictor, can recapitulate mutagenic trends within rhodopsin and can differentiate pathogenic transmembrane domain variants that cause it to misfold from those that compromise its signaling. This network also generates statistically significant predictions for the relative PME of transmembrane domain variants for another class A G protein-coupled receptor (β₂ adrenergic receptor) but not for an unrelated voltage-gated potassium channel (KCNQ1). Notably, our analyses of these networks suggest structural features alone are generally sufficient to recapitulate the observed mutagenic trends. Moreover, our findings imply that networks trained in this manner may be generalizable to proteins that share a common fold. Implications of our findings for the design of mechanistically specific genetic predictors are discussed.

损害整合膜蛋白质膜表达（PME）的错义突变是许多遗传疾病的根本原因。事实证明，将此类突变与特异性修饰折叠蛋白活性的突变区分开来，对于精准治疗的开发和靶向治疗非常有用。然而，预测突变对膜蛋白稳定性和/或表达的影响仍然具有挑战性。在这项工作中，我们利用深度突变扫描数据来训练一系列人工神经网络，以根据结构和/或进化特征预测 G 蛋白偶联受体跨膜域变体的 PME。我们证明，我们表现最好的网络（我们称之为 PME 预测器）可以概括视紫红质内的诱变趋势，并可以区分导致其错误折叠的致病性跨膜结构域变异与损害其信号传导的变异。该网络还为另一类 AG 蛋白偶联受体（β ₂肾上腺素受体）的跨膜结构域变体的相对 PME 生成统计显着的预测，但不为不相关的电压门控钾通道 (KCNQ1) 生成显着的预测。值得注意的是，我们对这些网络的分析表明，仅结构特征通常足以概括观察到的诱变趋势。此外，我们的研究结果表明，以这种方式训练的网络可以推广到共享共同折叠的蛋白质。讨论了我们的研究结果对机械特异性遗传预测因子设计的影响。