School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland, Australia.
Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
Protein Sci. 2024 Jun;33(6):e5000. doi: 10.1002/pro.5000.
G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.
G 蛋白偶联受体（GPCRs）是药物发现的最重要靶标家族之一。GPCR 研究的一个限制步骤是它们的稳定性，通常需要进行大量的蛋白质工程以稳定 GPCR，从而进行结构表征和药物筛选。不幸的是，使用球状可溶性蛋白开发的计算方法在 GPCR 的合理工程设计方面效果不佳。为了填补这一空白，我们提出了 GPCR-tm，这是一种新颖的、个性化的、基于结构的、基于网络的机器学习工具，用于研究突变对 GPCR 稳定性的影响。我们表明，GPCR-tm 的性能与其他方法一样好，甚至更好，并且可以准确地对各种类型的 A 类 GPCR 中发生的广泛突变的稳定性变化进行排序。GPCR-tm 在 10 倍交叉验证和盲测试集上分别实现了 0.74 和 0.46 的 Pearson 相关系数。我们观察到，基于（结构）图的特征是预测去稳定突变的最重要特征集，这表明这些特征正确地描述了突变发生的环境变化。更具体地说，GPCR-tm 能够根据突变对蛋白质稳定性的影响准确地对突变进行排序，从而指导其合理稳定。GPCR-tm 可通过用户友好的网络服务器 https://biosig.lab.uq.edu.au/gpcr_tm/ 使用。
