Hierarchical Contrastive Learning for Enzyme Function Prediction

Soorin Yim, Doyeong Hwang, Kiyoung Kim, Sehui Han

Keywords: Enzymatic Reaction, Protein Function Prediction, EC Number Prediction, Supervised Contrastive Learning

Abstract:

Enzymes are biological catalysts with numerous
industrial applications, and they are categorized
by the Enzyme Commission (EC) number system
based on their catalytic activities. With over
200 million protein sequences identified, experimental
characterization of enzymes is impractical,
necessitating computational methods. Current approaches
face challenges with class imbalance and
intrinsic hierarchy of the EC number system. This
study employs hierarchical contrastive learning
for EC number prediction, effectively integrating
the EC number hierarchy into the model. Our
approach addresses severe class imbalance and
improves prediction performance, particularly for
higher hierarchical levels and previously unseen
EC numbers, demonstrating enhanced robustness
and outperforming existing methods.

Submission Number: 78