AI-driven multi-modal framework revolutionizes protein editing for scientific and medical breakthroughs
Peer-Reviewed Publication
Updates every hour. Last Updated: 6-May-2025 06:09 ET (6-May-2025 10:09 GMT/UTC)
Researchers from Zhejiang University and HKUST (Guangzhou) have introduced ProtET, an innovative AI-powered multi-modal protein editing model published in Health Data Science. ProtET leverages advanced transformer-structured encoders and a hierarchical training paradigm to align protein sequences with natural language instructions, enabling precise and controllable protein editing.
The model was trained on over 67 million protein-biotext pairs from Swiss-Prot and TrEMBL databases and demonstrated significant improvements across key benchmarks, including 16.9% enhanced protein stability, optimized enzyme catalytic activity, and improved antibody-antigen binding affinity. ProtET’s zero-shot capabilities successfully designed SARS-CoV antibodies with stable 3D structures, highlighting its real-world biomedical applications.
This research represents a major advancement in AI-driven protein engineering, offering a scalable and interactive tool for scientific discovery, synthetic biology, and therapeutic development.
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