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BioinformaticsNature Biotechnology
Peer-reviewed journal article

ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning

Jin SuiD, Yan HeiD, Shiyang You, Shiyu Jason JiangiD, Xibin ZhouiD, Xuting ZhangiD, Yuxuan WangiD, Xining SuiD, Igor TolstoyiD, Xing ChangiD, Hongyuan LuiD, Fajie YuaniD

Abstract

ProTrek redefines protein exploration by seamlessly fusing sequence, structure, and natural language function (SSF) into an advanced tri-modal language model. Through contrastive learning, ProTrek bridges the gap between protein data and human understanding, enabling lightning-fast searches across nine SSF pairwise modality combinations. Trained on vastly larger datasets, ProTrek demonstrates quantum leaps in performance: (1) Elevating protein sequence-function interconversion by 30-60 fold; (2) Surpassing current alignment tools (i.e., Foldseek and MMseqs2) in both speed (100-fold acceleration) and accuracy, identifying functionally similar proteins with diverse structures; and (3) Outperforming ESM-2 in 9 of 11 downstream prediction tasks, setting new benchmarks in protein intelligence. These results suggest that ProTrek will become a core tool for protein searching, understanding, and analysis.

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02 /Specification & Classification

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