PDeepPP:A Deep learning framework with Pretrained Protein language for peptide classification

• URL: http://arxiv.org/abs/2502.15610v1
• Date: Fri, 21 Feb 2025 17:31:22 GMT
• Title: PDeepPP:A Deep learning framework with Pretrained Protein language for peptide classification
• Authors: Jixiu Zhai, Tianchi Lu, Haitian Zhong, Ziyang Xu, Yuhuan Liu, Xueying Wang, Dan Huang,
• Abstract summary: We propose a deep learning framework that integrates pretrained protein language models with a neural network combining transformer and CNN for peptide classification.<n>This framework was applied to multiple tasks involving PTM site and bioactive peptide prediction, utilizing large-scale datasets to enhance the model's robustness.
• Score: 6.55419985735241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Protein post-translational modifications (PTMs) and bioactive peptides (BPs) play critical roles in various biological processes and have significant therapeutic potential. However, identifying PTM sites and bioactive peptides through experimental methods is often labor-intensive, costly, and time-consuming. As a result, computational tools, particularly those based on deep learning, have become effective solutions for predicting PTM sites and peptide bioactivity. Despite progress in this field, existing methods still struggle with the complexity of protein sequences and the challenge of requiring high-quality predictions across diverse datasets. To address these issues, we propose a deep learning framework that integrates pretrained protein language models with a neural network combining transformer and CNN for peptide classification. By leveraging the ability of pretrained models to capture complex relationships within protein sequences, combined with the predictive power of parallel networks, our approach improves feature extraction while enhancing prediction accuracy. This framework was applied to multiple tasks involving PTM site and bioactive peptide prediction, utilizing large-scale datasets to enhance the model's robustness. In the comparison across 33 tasks, the model achieved state-of-the-art (SOTA) performance in 25 of them, surpassing existing methods and demonstrating its versatility across different datasets. Our results suggest that this approach provides a scalable and effective solution for large-scale peptide discovery and PTM analysis, paving the way for more efficient peptide classification and functional annotation.

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