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Parallel Convolutional Contrastive Learning Method for Enzyme Function Prediction
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DOI：10.1109/TCBB.2024.3447037.png)
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Abstract
En 中文
The function labeling of enzymes has a wide range of application value in the medical field, industrial biology and other fields. Scientists define enzyme categories by enzyme commission (EC) numbers. At present, although there are some tools for enzyme function prediction, their effects have not reached the application level. To improve the precision of enzyme function prediction, we propose a parallel convolutional contrastive learning (PCCL) method to predict enzyme functions. First, we use the advanced protein language model ESM-2 to preprocess the protein sequences. Second, PCCL combines convolutional neural networks (CNNs) and contrastive learning to improve the prediction precision of multifunctional enzymes. Contrastive learning can make the model better deal with the problem of class imbalance. Finally, the deep learning framework is mainly composed of three parallel CNNs for fully extracting sample features. we compare PCCL with state-of-art enzyme function prediction methods based on three evaluation metrics. The performance of our model improves on both two test sets. Especially on the smaller test set, PCCL improves the AUC by 2.57%.
Keywords:
Enzymes
Contrastive learning
Proteins
Training
Predictive models
Computational modeling
Feature extraction
Bioinformatics
contrastive learning
enzyme function prediction
parallel convolution
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3.3K
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