Protein Captioning: Bridging the Gap between Protein Sequences and Natural Languages

We introduce the multimodal task of Protein Captioning , which is an easy-to-understand and flexible way for protein analysis. Compared to specific protein recognition or classification tasks, such as enzyme reaction classification and gene ontology term prediction, protein captioning provides comprehensive textural descriptions for proteins, thus playing a key role in bridging the gap between protein sequences and natural languages. To address the problem, we propose a simple yet effective method, Protein-to-Text Generative Pre-Trained Transformer (P2T-GPT), to fuse multimodal embeddings and translate the chain of amino acid residues in a protein to a sequence of natural language words, i.e., text. For the evaluation of protein captioning, we collect the ProteinCap dataset that contains 94,454 protein-text pairs. Experiments on ProteinCap demonstrate the effectiveness of the proposed P2T-GPT on protein captioning. For example, our method obtains improvements of 8.74, 10.03, and 11.05 in the BERTScore compared to the baseline model on ProteinCap- \(\alpha,\beta,\gamma\) , respectively. As minor contributions, first, P2T-GPT provides a way to connect protein science and Large Language Models (LLMs). By appending ChatGPT, our method can interact in a conversational way to answer questions given a protein. Second, we show that protein captioning can be treated as a pre-trained task that can benefit a range of downstream tasks, to a certain extent.