AI-Driven Protein Research: From Prediction to Design.

Proteins are central to nearly all biological processes, mediating enzymatic catalysis, structural scaffolding, and molecular signaling. Deciphering how linear amino acid sequences encode three-dimensional conformations and biological functions has long been a defining challenge in molecular life sciences. Traditional structural biology approaches, such as X-ray crystallography and cryo-electron microscopy, have established the field, yet their high cost, labor intensity, and limited throughput restrict comprehensive coverage of the proteome. The exponential expansion of protein sequence databases, paired with advances in artificial intelligence (AI) and deep learning, has drastically accelerated our ability to predict, annotate, and even design proteins. In this mini review, we trace the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling. Throughout, we highlight key conceptual advances and their translational implications for biomedical science and biotechnology.