FoldToken: Learning Protein Language via Vector Quantization and Beyond

Is there a foreign language describing protein sequences and structures simultaneously? Protein structures, represented by continuous 3D points, have long posed a challenge due to the contrasting modeling paradigms of discrete sequences. We introduce FoldTokenizer to represent protein sequence-structure as discrete symbols. This approach involves projecting residue types and structures into a discrete space, guided by a reconstruction loss for information preservation. We name the learned discrete symbols as FoldToken, and the sequence of FoldTokens serves as a new protein language, transforming the protein sequence-structure into a unified modality. We apply the created protein language on general backbone inpainting task, building the first GPT-style model (FoldGPT) for sequence-structure co-generation with promising results. Key to our success is the substantial enhancement of the vector quantization module, Soft Conditional Vector Quantization (SoftCVQ).

Authors

• Zhangyang Gao Westlake University
• Cheng Tan Zhejiang University Westlake University
• Jue Wang
• Yufei Huang Westlake University
• Lirong Wu Westlake University
• Stan Z. Li Westlake University

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