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Leveraging pretrained deep protein language model to predict peptide collision cross section.

Ayano Nakai-Kasai1, Kosuke Ogata2, Yasushi Ishihama3,4

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This study introduces a new deep learning model for predicting peptide collision cross section (CCS) in proteomics. The model significantly reduces training time and computational cost while maintaining high prediction accuracy.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Computational Biology

Background:

  • Collision cross section (CCS) is a key parameter in ion mobility spectrometry (IMS) coupled with liquid chromatography/tandem mass spectrometry (LC-MS/MS) for peptide separation.
  • Accurate CCS prediction is crucial for advancing proteomics workflows, especially for complex samples with longer peptides and higher charge states.

Purpose of the Study:

  • To develop and validate a novel prediction model for peptide collision cross section (CCS) that addresses challenging prediction tasks.
  • To leverage a pretrained deep protein language model for efficient and accurate CCS prediction.

Main Methods:

  • Utilized a pretrained deep protein language model as a feature extractor for CCS prediction.
  • Developed a new prediction model incorporating this pretrained model, contrasting it with conventional methods requiring training from scratch.
  • Generated and used novel experimental data for model training and performance evaluation.

Main Results:

  • The proposed model significantly reduced training time compared to conventional methods.
  • The model achieved comparable or improved prediction performance for CCS, even for longer peptides with higher charge states.
  • Demonstrated a more computationally efficient and potentially "greener" approach to predicting peptide properties.

Conclusions:

  • The pretrained deep protein language model approach offers a faster and effective method for peptide CCS prediction in LC-MS/MS proteomics.
  • This method enhances the feasibility of advanced proteomics workflows by enabling efficient prediction of crucial peptide properties.
  • The study highlights a more sustainable computational strategy for proteomic data analysis.