Interpretable Enzyme Function Prediction via Residue-Level Detection

• URL: http://arxiv.org/abs/2501.05644v2
• Date: Fri, 06 Jun 2025 00:19:56 GMT
• Title: Interpretable Enzyme Function Prediction via Residue-Level Detection
• Authors: Zhao Yang, Bing Su, Jiahao Chen, Ji-Rong Wen,
• Abstract summary: We present an attention-based framework, namely ProtDETR, for enzyme function prediction.<n>It uses a set of learnable functional queries to adaptatively extract different local representations from the sequence of residue-level features.<n>ProtDETR significantly outperforms existing deep learning-based enzyme function prediction methods.
• Score: 58.30647671797602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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