The extraordinary diversity of protein sequences and structures gives rise to a vast protein functional universe with extensive biotechnological potential. Nevertheless, this universe remains largely unexplored, constrained by the limitations of natural evolution and conventional protein engineering. Substantial evidence further indicates that the known natural fold space is approaching saturation, with novel folds rarely emerging. AI-driven de novo protein design is overcoming these constraints by enabling the computational creation of proteins with customized folds and functions. This review systematically surveys the rapidly advancing field of AI-based de novo protein design, reviewing current methodologies and examining how cutting-edge computational frameworks accelerate discovery through three complementary vectors: (1) exploring novel folds and topologies; (2) designing functional sites de novo; (3) exploring sequence–structure–function landscapes. We highlight key applications across therapeutic, catalytic, and synthetic biology and discuss the persistent challenges. By fusing recent progress and the existing limitations, this review outlines how AI is not only accelerating the exploration of the protein functional universe but also fundamentally expanding the possibilities within protein engineering, paving the way for bespoke biomolecules with tailored functionalities.
Zhang et al. (2025) studied this question.