Ab-initio amino acid sequence design from protein text description with ProtDAT

Xiao-Yu Guo, Yi-Fan Li, Yuan Liu, Xiaoyong Pan and Hong-Bin Shen ()
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Xiao-Yu Guo: Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing
Yi-Fan Li: Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing
Yuan Liu: Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing
Xiaoyong Pan: Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing
Hong-Bin Shen: Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Protein design has become a critical method in advancing significant potential for various applications such as drug development and enzyme engineering. However, protein design methods utilizing large language models with solely pretraining and fine-tuning struggle to capture relationships in multi-modal protein data. To address this, we propose ProtDAT, a de novo fine-grained multi-modal data interaction framework capable of designing proteins from descriptive protein text input. ProtDAT builds upon the inherent characteristics of protein data to unify sequences and text as a cohesive whole rather than separate entities. It leverages a novel Multi-modal Cross-attention, integrating protein sequences and textual information for a foundational level and seamless integration. Evaluation metrics such as pLDDT, TM-score and RMSD are implemented to evaluate the structural plausibility, functionality, structural similarity, and validity of protein sequences. Experiments on 20,000 text-sequence pairs from Swiss-Prot within the ProtDAT framework demonstrate higher accuracy compared to the performance of the best method in the experiments, with a 23.34% increase in pLDDT, a 76.45% increase in TM-score, and a 24.41% reduction in RMSD.

Date: 2025
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DOI: 10.1038/s41467-025-65562-w

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