Machine learning for predicting protein properties: A comprehensive review

In the field of protein engineering, the function and structure of proteins are key to understanding cellular mechanisms, biological evolution, and biodiversity. With the advancement of high-throughput sequencing technologies, we have accumulated a vast amount of protein sequence data, yet the protein properties and functional information contained within these data have not been fully deciphered. Predicting protein properties is crucial for revealing how proteins function within complex biological systems and also offers possibilities for the early diagnosis of diseases and the development of new drugs. However, due to the complexity of protein properties and functions, traditional experimental methods face significant challenges in terms of cost, time, and accuracy. In recent years, machine learning techniques have become a powerful tool for addressing these challenges due to their ability to learn patterns and relationships from large-scale data. Machine learning methods have demonstrated outstanding performance in areas such as protein structure prediction, function annotation, interaction recognition, and physicochemical property prediction. This survey reviews the application of machine learning in protein property prediction. Current research progress, challenges in the field, and future development directions have been discussed, highlighting the significance and potential of machine learning methods in advancing protein science research and applications.