Protein Structure Prediction: Methods and Advancements

Protein structure prediction is pivotal for understanding biological functions and disease mechanisms. This review systematically examines mainstream computational approaches, including homology modeling, fold recognition, ab initio prediction, and AI-assisted techniques. Traditional methods such as homology modeling construct structures through template matching, while fold recognition leverages the limited fold universe for sequence-structure alignment. Ab initio prediction explores conformational space based on physical principles but faces computational complexity constraints. Recently, deep learning models represented by AlphaFold have achieved breakthroughs by integrating evolutionary constraints and 3D coordinate generation, with experimental-grade accuracy for single-domain proteins (e.g., AlphaFold2). AlphaFold3 further extends capabilities to multi-molecular complexes. Despite becoming the preferred choice for researchers, AI methods still exhibit deviations from physiological conformations. Future advancements require fundamental innovations integrating physical principles to enhance applications in drug design and gene editing.