  In recent years, significant progress has been made in the field of protein
function prediction with the development of various machine-learning
approaches. However, most existing methods formulate the task as a
multi-classification problem, i.e. assigning predefined labels to proteins. In
this work, we propose a novel approach, Prot2Text, which predicts a protein's
function in a free text style, moving beyond the conventional binary or
categorical classifications. By combining Graph Neural Networks(GNNs) and Large
Language Models(LLMs), in an encoder-decoder framework, our model effectively
integrates diverse data types including protein sequence, structure, and
textual annotation and description. This multimodal approach allows for a
holistic representation of proteins' functions, enabling the generation of
detailed and accurate functional descriptions. To evaluate our model, we
extracted a multimodal protein dataset from SwissProt, and demonstrate
empirically the effectiveness of Prot2Text. These results highlight the
transformative impact of multimodal models, specifically the fusion of GNNs and
LLMs, empowering researchers with powerful tools for more accurate function
prediction of existing as well as first-to-see proteins.
