Efficient Marginalization to Compute Protein Posterior Probabilities From Shotgun Mass Spectrometry Data

J Proteome Res. 2010 Oct 1;9(10):5346-57. doi: 10.1021/pr100594k.

Abstract

The problem of identifying proteins from a shotgun proteomics experiment has not been definitively solved. Identifying the proteins in a sample requires ranking them, ideally with interpretable scores. In particular, "degenerate" peptides, which map to multiple proteins, have made such a ranking difficult to compute. The problem of computing posterior probabilities for the proteins, which can be interpreted as confidence in a protein's presence, has been especially daunting. Previous approaches have either ignored the peptide degeneracy problem completely, addressed it by computing a heuristic set of proteins or heuristic posterior probabilities, or estimated the posterior probabilities with sampling methods. We present a probabilistic model for protein identification in tandem mass spectrometry that recognizes peptide degeneracy. We then introduce graph-transforming algorithms that facilitate efficient computation of protein probabilities, even for large data sets. We evaluate our identification procedure on five different well-characterized data sets and demonstrate our ability to efficiently compute high-quality protein posteriors.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Animals
  • Bacterial Proteins / analysis
  • Bayes Theorem
  • Caenorhabditis elegans / metabolism
  • Caenorhabditis elegans Proteins / analysis
  • Haemophilus influenzae / metabolism
  • Mass Spectrometry / methods*
  • Probability
  • Proteins / analysis*
  • Proteomics / methods*
  • Reproducibility of Results
  • Saccharomyces cerevisiae / metabolism
  • Saccharomyces cerevisiae Proteins / analysis

Substances

  • Bacterial Proteins
  • Caenorhabditis elegans Proteins
  • Proteins
  • Saccharomyces cerevisiae Proteins