Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, California.
Discovery Sciences, Pfizer, Groton, Connecticut.
Biophys J. 2019 Dec 3;117(11):2228-2239. doi: 10.1016/j.bpj.2019.10.023. Epub 2019 Oct 24.
Although the three-dimensional structures of G-protein coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence-, structure-, and dynamics-based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available data set. A blind prediction for thermostable mutations of the complement factor C5a receptor 1 retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.
虽然 G 蛋白偶联受体 (GPCRs) 的三维结构（GPCRs 是最大的药物靶点超家族）使得基于结构的药物设计成为可能，但仍有 87%的 GPCR 没有结构信息。这是因为纯化固有灵活的 GPCRs 具有很大的挑战性。通过系统的丙氨酸扫描突变来识别热稳定突变的 GPCR 一直是稳定 GPCR 的成功策略，但对于每个 GPCR 来说仍然是一项艰巨的任务。我们开发了一种计算方法，该方法结合了 GPCR 的序列、结构和动力学分子特性，以再现 GPCR 的稳定性，并结合了四种不同的机器学习方法，在实验之前预测热稳定突变。该方法已经在 1231 个突变体的热稳定性数据上进行了训练，这是最大的公开可用数据集。与使用系统丙氨酸扫描的前 50 次尝试中只有 3%的热稳定突变排在前 50 位相比，针对补体因子 C5a 受体 1 的热稳定突变的盲预测中，有 36%的热稳定突变排在前 50 位。
