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.
Guo et al. (Wed,) studied this question.