Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

预测错义突变引起的蛋白质稳定性变化的计算方法在过去几十年中取得了很大进展。然而，大多数可用方法在预测稳定突变方面的准确性非常有限，因为现有的实验集主要是降低蛋白质稳定性的突变。此外，很少有方法能够在不同的测试用例中始终表现良好。为了解决这些问题，我们开发了一种新的计算方法 PremPS 来更准确地评估错义突变对蛋白质稳定性的影响。 PremPS 方法仅由十个基于进化和结构的特征组成，并在具有相同数量的稳定和不稳定突变的平衡数据集上进行参数化。在九个基准数据集上对 PremPS 与其他可用方法的预测性能进行全面比较，证实我们的方法始终优于其他方法，并且在估计稳定突变的影响方面显示出相当大的改进。蛋白质可能具有多种可用结构，如果使用同一蛋白质的另一种结构，则基于结构的方法预测的稳定性变化可能会有所不同。因此，我们进一步估计了使用不同结构对预测准确性的影响，并证明我们的方法在不同类型的结构中表现良好，除了低分辨率结构和基于低序列同一性模板构建的模型。 PremPS 可用于寻找功能上重要的变异，揭示功能影响和蛋白质设计的分子机制。 PremPS 可在 https://lilab.jysw.suda.edu 免费获取。cn/research/PremPS/，它允许进行大规模突变扫描，大约需要四分钟才能对每个具有约 300 个残基的蛋白质的单个突变进行计算，并且每个额外突变需要约 0.4 秒。