Pool PaRTI: A PageRank-based Pooling Method for Robust Protein Sequence Representation in Deep Learning

Protein language models produce token-level embeddings for each residue, resulting in an output matrix with dimensions that vary based on sequence length. However, downstream machine learning models typically require fixed-length input vectors, necessitating a pooling method to compress the output matrix into a single vector representation of the entire protein. Traditional pooling methods often result in substantial information loss, impacting downstream task performance. We aim to develop a pooling method that produces more expressive general-purpose protein embedding vectors while offering biological interpretability. We introduce Pool PaRTI, a novel pooling method that leverages internal transformer attention matrices and PageRank to assign token importance weights. Our unsupervised and parameter-free approach consistently prioritizes residues experimentally annotated as critical for function, assigning them higher importance scores. Across four diverse protein machine learning tasks, Pool PaRTI enables significant performance gains in predictive performance. Additionally, it enhances interpretability by identifying biologically relevant regions without relying on explicit structural data or annotated training. To assess generalizability, we evaluated Pool PaRTI with two encoder-only protein language models, confirming its robustness across different models. Pool PaRTI is implemented in Python with PyTorch and is available at https: //github. com/Helix-Research-Lab/PoolPaRTI. git. The Pool PaRTI sequence embeddings and residue importance values for all human proteins on UniProt are available at https: //zenodo. org/records/15036725 for ESM2 and protBERT.