Recent advances in deep learning have fundamentally transformed protein research by creating a synergistic cycle that connects structure prediction, functional annotation, and rational design. This review presents an integrative framework to demonstrate how breakthroughs in one domain catalytically enable progress in the next. First, for a broad range of single-domain, globular proteins-particularly those with sufficient evolutionary information-end-to-end deep learning models exemplified by AlphaFold2 have achieved near-experimental accuracy, effectively addressing a core challenge in structural biology and generating an unprecedented repository of high-confidence predicted structures. This vast structural substrate then serves as the essential foundation for the "Understand" phase, where multimodal models increasingly integrate 3D coordinates with sequence and interaction data to achieve precise, mechanism-aware functional predictions that transcend homology-based inference. These deep functional insights, in turn, provide the critical biochemical constraints for the final "Create" phase. Here, generative AI and inverse folding models enable the de novo design of novel proteins-from enzymes to therapeutics-with tailored structures and functions, guided by desired activity blueprints. This self-reinforcing cycle is further amplified by hybrid experimental-computational workflows, such as cryo-EM integrated with AI, which resolve complex and dynamic assemblies. While challenges in data scarcity, interpretability, and out-of-distribution generalization persist, this unified "predict-understand-create" paradigm establishes deep learning as the cornerstone of a new era in protein science. It not only accelerates discovery in drug development and synthetic biology but also transforms the field from descriptive observation to principled, programmable engineering of biomolecular function.
扫码关注我们
求助内容:
应助结果提醒方式:
