Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, China.
Proteomics. 2024 Jun;24(12-13):e2300371. doi: 10.1002/pmic.202300371. Epub 2024 Apr 21.
Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.
预测由变异引起的蛋白质稳定性变化具有重要意义。提高蛋白质的热稳定性对于生物医学和工业应用非常重要。本文综述了预测突变对蛋白质稳定性影响的最新方法、包含蛋白质突变和热力学参数的数据库以及在高通量环境中有效评估蛋白质稳定性的实验技术。介绍了各种用于蛋白质稳定性预测的公共数据库。此外，还综述了预测由于变异引起的蛋白质稳定性变化的最先进的计算方法。每种方法的特征类型、基础算法和预测结果也都有详细说明。此外，还介绍了一些用于验证计算方法预测结果的实验方法。最后，本文总结了蛋白质稳定性预测的进展和挑战，并讨论了未来研究方向的潜在模型。
