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Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models.

Ufuk Kirik1, Jan C Refsgaard1,2, Lars J Jensen1

  • 1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Science , University of Copenhagen , Blegdamsvej 3B , DK-2200 Copenhagen , Denmark.

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|May 11, 2019
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Summary

This study introduces a novel hidden Markov model to predict peptide fragmentation patterns in tandem mass spectrometry. This improves the accuracy of peptide identification in proteomics by better analyzing experimental spectra.

Keywords:
HMMbottom-up proteomicshidden Markov modelsmachine learningpeptide fragmentation inferencepeptide identificationsoftwarestochastic modelingtandem mass spectrometry

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Computational Biology

Background:

  • Tandem mass spectrometry (MS/MS) is crucial for high-throughput proteomics.
  • Current peptide spectrum matching algorithms assume uniform fragment ion intensity, which is inaccurate.
  • MS/MS spectra exhibit reproducible fragmentation patterns influenced by peptide sequence and charge state.

Purpose of the Study:

  • To develop a novel algorithm for predicting peptide fragmentation patterns in MS/MS spectra.
  • To improve the accuracy of peptide identification in quantitative proteomics.
  • To enhance the statistical power of peptide spectrum matching.

Main Methods:

  • Utilized millions of MS/MS spectra for training.
  • Developed a novel prediction algorithm based on hidden Markov models (HMMs).
  • Trained an interpolated-HMM model for efficient pattern recognition.

Main Results:

  • The HMM model successfully identified meaningful patterns in peptide fragmentation.
  • Prediction performance remained robust across different training/testing data splits.
  • The model effectively discerns unlikely intense fragment ions for a given peptide.

Conclusions:

  • The proposed HMM-based model accurately captures peptide fragmentation patterns.
  • This model serves as a valuable preprocessing step for peptide identification.
  • Incorporating this model enhances the statistical basis of peptide spectrum matching in proteomics.