From Traditional Protein Engineering to AI-Driven Design: Principles, Methods, and Applications

Abstract

Protein engineering is a critical technology that modifies protein sequences and structures to optimize specific functions or introduce new ones, and it is widely applied in biomanufacturing, pharmaceutical research and development, synthetic biology and other fields. Traditional protein engineering mainly relies on the modification of natural proteins, and its strategies usually include simulating natural evolution processes or designing based on protein structures, with the aim of enhancing protein stability, activity and other properties. However, the high complexity of protein sequences and the limited understanding of the correspondence between amino acid sequences, protein structures and biological functions have restricted the further development of traditional methods. In recent years, the rapid development of artificial intelligence has greatly driven the paradigm shift in protein engineering research, enabling researchers to efficiently explore proteins with specific structures and functions in a broader sequence space. This paper reviews the basic principles and development history of protein engineering, and introduces traditional strategies such as directed evolution and rational design. It focuses on protein structure prediction tools represented by AlphaFold, generative models such as RFdiffusion and ProteinMPNN, and the de novo protein design methods driven by these tools. Furthermore, combined with classic applications of protein engineering including metabolic engineering, enzyme engineering and antibody engineering, this paper analyzes the progress of artificial intelligence in protein design and optimization. By sorting out relevant studies, this paper aims to provide a reference for understanding the development context of protein engineering and the application of artificial intelligence in this field.