Transformation of Protein Design: From Traditional Approaches to AI-Driven Precision Engineering

Abstract

Proteins play a central role in processes such as catalysis, mass transport, and signal transduction. However, natural proteins cannot fully meet human needs, making protein design a critical frontier technology in the 21st century life sciences. With the development of artificial intelligence (AI) and deep learning, protein design has undergone a revolutionary transformation. Traditional methods relying on experience and trial and error have been gradually replaced by computational simulations and intelligent algorithms, significantly improving the accuracy of structure prediction and design efficiency. This article systematically reviews the basic principles and common methods of protein design, focusing on the applications and advantages of representative AI models such as AlphaFold3, RoseTTAFoldNA, and OmegaFold in protein structure prediction and the development of novel functional proteins. Research has demonstrated that AI tools can not only break through the limitations of the traditional “backbone-first, sequence-later” approach, achieving coordinated optimization of sequence and structure, but also significantly shorten R&D cycles, reduce costs, and promote the industrialization of drug discovery and synthetic biology. However, current AI design still faces challenges such as insufficient dynamic conformational simulations, lack of functional data, and unclear patent ownership. In the future, with the integration of multimodal data and the improvement of models’ ability to capture dynamic behavior, AI-driven protein design is expected to show broad prospects in basic research and clinical translation.