Protein science is undergoing an AI-driven cognitive revolution that has revolutionized the way we understand and design biological macromolecules. Deep learning algorithms, such as AlphaFold2 and RoseTTAFold, have solved the "protein folding problem" that has plagued the biological community for five decades and can predict the three-dimensional structure of proteins with atomic-level accuracy. However, current cutting-edge research has moved beyond 'prediction' to 'generation'. Using generative AI models, researchers can now "out of thin air" design entirely new protein skeletons with specific topologies and functions that do not exist in nature. This "reverse folding" capability allows scientists to design high-affinity binding proteins or microdrugs from scratch for specific drug targets. In synthetic biology scenarios, AI is used to optimize the catalytic activity and stability of enzymes. By analyzing massive sequence-functional data through machine learning, the algorithm can predict key non-natural mutation sites, enabling industrial enzymes to maintain efficient catalysis at extreme pH or high temperatures, greatly reducing biofabrication energy consumption. The combination of AI and automated experimental platforms has shortened the R&D cycle for protein engineering from years to months, opening the era of intelligent biomanufacturing.