<p>Protein language models are trained to predict amino acid sequences from vast protein databases and learn to represent proteins as feature vectors. These vector representations have enabled impressive applications, from predicting mutation effects to protein folding. One of the reasons offered for the success of these models is that conserved sequence motifs tend to be important for protein fitness. Yet, the relationship between sequence conservation and fitness can be confounded by the evolutionary and environmental context. Should we, therefore, look to other data sources that may contain more direct functional information? In this work, we conduct a comprehensive study examining the effects of training protein models to predict 19 types of text annotation from UniProt. Our results show that fine-tuning protein models on a subset of these annotations enhances the models’ predictive capabilities on a variety of function prediction tasks. In particular, when evaluated on our tasks, our model outperforms the basic local alignment search tool, which none of the pretrained protein models accomplished. Our results suggest that a much wider array of data modalities, such as text annotations, may be tapped to improve protein language models.</p>