Interpretable Enzyme Function Prediction via Residue-Level Detection

Zhao Yang; Bing Su; Jiahao Chen; Ji-Rong Wen

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arXiv · 2501.05644

Interpretable Enzyme Function Prediction via Residue-Level Detection

Zhao Yang·Bing Su·Jiahao Chen·Ji-Rong Wen

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Abstract

Predicting multiple functions labeled with Enzyme Commission (EC) numbers from the enzyme sequence is of great significance but remains a challenge due to its sparse multi-label classification nature, i.e., each enzyme is typically associated with only a few labels out of more than 6000 possible EC numbers. However, existing machine learning algorithms generally learn a fixed global representation for each enzyme to classify all functions, thereby they lack interpretability and the fine-grained information of some function-specific local residue fragments may be overwhelmed. Here we present an attention-based framework, namely ProtDETR (Protein Detection Transformer), by casting enzyme function prediction as a detection problem. It uses a set of learnable functional queries to adaptatively extract different local representations from the sequence of residue-level features for predicting different EC numbers. ProtDETR not only significantly outperforms existing deep learning-based enzyme function prediction methods, but also provides a new interpretable perspective on automatically detecting different local regions for identifying different functions through cross-attentions between queries and residue-level features. Code is available at https://github.com/yangzhao1230/ProtDETR.

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BibTeX RIS

Zhao Yang, Bing Su, Jiahao Chen, Ji-Rong Wen. 2025-06-06. Interpretable Enzyme Function Prediction via Residue-Level Detection. https://arxiv.org/abs/2501.05644

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