Knowledge-based Dual External Attention Network for peptide detectability prediction

Abstract

Accurate prediction of peptide detectability plays a crucial role in various proteomics data analyses such as peptide identification and protein quantification. To improve the precision of peptide detectability prediction, we propose a novel approach called the Knowledge-based Dual External Attention Network (KDEAN). KDEAN introduces several innovative elements to enhance its representation and prediction capabilities. Firstly, it extracts valuable knowledge-based features from peptide sequences to facilitate the pattern recognition process. Secondly, KDEAN adopted dual networks to separately train peptide sequences from both the forward and backward directions, capturing comprehensive information. Thirdly, an external attention mechanism is utilized to identify and understand the connections between different peptide samples. The structure of KDEAN enables long-term dependency learning from both directions of the peptide sequences. Extensive evaluations on four testing datasets demonstrate that KDEAN outperforms existing methods, achieving a higher average performance in peptide detectability prediction. Additionally, comprehensive ablation studies confirm the effectiveness and advantages of key components in KDEAN, including knowledge-based feature representation, dual network architecture, and external attention.