Affiliation:
1. 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
2. School of Mathematics and Statistics Guangdong University of Technology Guangzhou China
3. School of Life Sciences Shanghai University Shanghai China
Abstract
AbstractForecasting 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.
Funder
National Key Research and Development Program of China