# Protein function prediction

EZpred carries the homolog-based features long used in protein structure prediction into enzyme function prediction, taking only sequence features from homologs and ignoring their function labels. Most deep learning models for this task read features from the target sequence alone, and the few that use homologs only aggregate labeled ones. On 720 enzymes with new EC numbers in UniProt 2024_02, EZpred reaches an F1 of 0.786 for full four-digit EC numbers, 27% and 31% above CLEAN and GraphEC.

EZpred carries the homolog-based features long used in protein structure prediction into enzyme function prediction, taking only sequence features from homologs and ignoring their function labels. Most deep learning models for this task read features from the target sequence alone, and the few that use homologs only aggregate labeled ones. On 720 enzymes with new EC numbers in UniProt 2024_02, EZpred reaches an F1 of 0.786 for full four-digit EC numbers, 27% and 31% above CLEAN and GraphEC.