AIRPred: A Deep Learning Model Predictor for Peptide Intensity Ratios in Cross-Linking Mass Spectrometry Improves Cross-Link Spectrum Matching.

ABSTRACT: Cross-linking mass spectrometry (XL-MS) is a powerful tool in structural proteomics, offering insights into protein conformations, interactions and dynamics by linking spatially proximal residues. However, current cross-linked spectrum match (CSM) scoring methods rely heavily on mass-to-charge ratio (m/z) comparisons, often neglecting fragment ion intensity information, which limits their ability to accurately distinguish true CSMs from false positives. To overcome this limitation, we present AIRPred, a deep learning model that predicts intensity ratios between cross-linked peptide pairs to improve CSM identification. AIRPred employs convolutional neural network (CNN) blocks to capture peptide fragmentation patterns and an attention layer to model peptide interactions. Our re

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12658862 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature