Enzyme Classification via Semi-Supervised Functional Residue Learning

Abstract Predicting enzymatic function from a protein sequence is a fundamental task in protein discovery and engineering. In this paper, we present Semi-supervised Learning for Enzyme Classification (SLEEC): a semi-supervised learning framework that learns a function-aware protein representation for Enzyme Commision (EC) number prediction. SLEEC achieves SOTA performance on standard bench-marks and provides interpretable, residue-level annotations. We further demonstrate that our framework is robust to benign sequence modifications routinely observed in protein engineering workflows– such as appending functional tags– a desirable property that current ML frameworks lack. Our main technical contribution is a multiple sequence alignment (MSA)-based data augmentation technique for discovering sparse residue activations within a given enzyme sequence. Competing Interest Statement The authors have declared no competing interest. Footnotes dz367{at}rutgers.edu danny.diaz{at}utexas.edu