How to Integrate Non-Sequence Features for Peptide Collision Cross Section Prediction with Deep Learning?

The collision cross section (CCS) is a crucial parameter that reflects a molecule’s size and shape, offering valuable insights into its structural conformation. Accurate CCS predictions can significantly enhance the identification and separation of compounds in mass spectrometry, proving particularly useful in fields such as proteomics and drug discovery. The CCS of a peptide is a reproducible physicochemical property influenced by its mass, charge, and gas-phase structure. Despite its importance, predicting CCS using both sequence data and physical attributes remains challenging. To address this, we introduce PEP2CCS, a deep learning model designed to predict CCS. This model integrates enhanced physical features of peptides, thereby improving the accuracy of CCS predictions. The analysis revealed that PEP2CCS had a Median Absolute Percent Error of 1.139% in predicting the synthetic proteomic peptide cross-section, which is 18.6% lower than state-of-the-art method. Furthermore, the results suggest that the integration of the charge state into the model is a significant contributor to the accurate prediction of CCS values. Code and pre-trained model are released at https://github.com/xfcui/PEP2CCS.