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BITS - Protein inference from mass spectrometry data

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This is the fifth presentation of the BITS training on 'Mass spec data processing'. It reviews the problems of determining protein sequences of mass spec data, how to deal with it, with an overview of useful tools. Thanks to the Compomics Lab of the VIB for their contribution.

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peptide validation and protein inference kenny helsens kenny.helsens@ugent.be Lennart MARTENS lennart.martens@ebi.ac.uk Computational Omics and Systems Biology Group Proteomics Services Group European Bioinformatics Institute Department of Medical Protein Research, VIB Hinxton, Cambridge United Kingdom Department of Biochemistry, Ghent University www.ebi.ac.uk Kenny Helsens Ghent, Belgium BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Data processing and information ambiguity Raw data Peaklists Peptide sequences Protein accession numbers ambiguity data size See: Martens and Hermjakob, Molecular BioSystems, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
PEPTIDE IDENTIFICATION VALIDATION Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Populations and individuals 10,000 peptide-to-spectrum matches 5% decoy hits Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Eliminating false positives Suspect peptide identifications happen. The problem is that finding them requires detailed analysis of a single spectrum and its identifications, amongst thousands of other spectra… Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Automated interpretation The Netherlands?? Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Manual interpretation Tyrosine phosporylation See: Ghesquière and Helsens, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Peptizer expert system Agent c Agent b Agent d Agent a Agent e Vote casts +1 +1 0 -1 +1 Aggregation of the votes Confident Peptide Identifications Suspicious Trusted subset subset See: Helsens et al, Molecular and Cellular Proteomics, 2008 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Peptizer expert system See: Helsens et al, Molecular and Cellular Proteomics, 2008 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Peptizer expert system See: Helsens et al, Molecular and Cellular Proteomics, 2008 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
PROTEIN INFERENCE Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Not all peptides are created equal Gene 1a 1b 2 3 4 5 6a 6b Transcripts 1a 1b 2 5 6a 6b 1a 1b 2 3 5 6a 6b 1b 2 3 4 5 6a 6b 1a 1b 2 3 4 5 6a Translations 2 5 2 3 5 Peptides 2 3 4 5 matching all transcripts 2 3 4 5 redundant matching a transcript subset matching exactly 1 translation Intron Exon UTR Exon CDS Peptide Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Sample preparation consequences See: Nesvizhskii AI et al, Molecular and Cellular Proteomics, 2005 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Sample preparation consequences See: Nesvizhskii AI et al, Molecular and Cellular Proteomics, 2005 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Protein inference: a question of conviction peptides a b c d proteins prot X x x Minimal set Occam { prot Y prot Z x x x x peptides a b c d proteins prot X x x Maximal set anti-Occam { prot Y prot Z x x x x peptides a b c d proteins prot X (-) x x Minimal set with maximal annotation { prot Y (+) prot Z (0) x x x x true Occam? See: Martens and Hermjakob, Molecular BioSystems, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
ALGORITHMS FOR THE PROTEIN INFERENCE PROBLEM Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
A few algorithms for protein inference • IDPicker Zhang et al, Journal of Proteome Research, 2007 • ProteinProphet Nesvizhskii AI et al, Analytical Chemistry, 2003 • DBToolkit Martens et al, Bioinformatics, 2005 http://genesis.UGent.be/dbtoolkit Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
IDPicker parsimonious protein assembly (I) Initialize See: Zhang et al, Journal of Proteome Research, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
IDPicker parsimonious protein assembly (II) Collapse See: Zhang et al, Journal of Proteome Research, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
IDPicker parsimonious protein assembly (III) Separate See: Zhang et al, Journal of Proteome Research, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
IDPicker parsimonious protein assembly (IV) Reduce See: Zhang et al, Journal of Proteome Research, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
ProteinProphet: the simplified view peptide peptide protein probability weight probability peptide probability In iteration 1, all weights w start off as 1/n, with n the degeneracy count for the peptide See: Nesvizhskii AI et al., Analytical Chemistry, 2003 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
DBToolkit protein inference peptides a b cd proteins prot X (-) x x Minimal set with maximal annotation { prot Y (+) prot Z (0) x x x x Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Some indications from the HUPO BPP peptides a b c d proteins prot X (-) x x prot Y (+) x prot Z (0) x x x Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
PROTEIN INFERENCE AND QUANTIFICATION Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Some inference examples (i) http://genesis.ugent.be/rover/ Nice and easy, 1/1, only unique peptides (blue) and a narrow distribution See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Some inference examples (ii) http://genesis.ugent.be/rover/ Nice and easy, down-regulated See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Some inference examples (iii) http://genesis.ugent.be/rover/ A little less easy, up-regulated See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Some inference examples (iv) http://genesis.ugent.be/rover/ A nice example of the mess of degenerate peptides See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Some inference examples (v) http://genesis.ugent.be/rover/ A bit of chaos, but a defined core distribution See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
Thank you! Questions? Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011

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BITS - Protein inference from mass spectrometry data

  • 2. peptide validation and protein inference kenny helsens kenny.helsens@ugent.be Lennart MARTENS lennart.martens@ebi.ac.uk Computational Omics and Systems Biology Group Proteomics Services Group European Bioinformatics Institute Department of Medical Protein Research, VIB Hinxton, Cambridge United Kingdom Department of Biochemistry, Ghent University www.ebi.ac.uk Kenny Helsens Ghent, Belgium BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 3. Data processing and information ambiguity Raw data Peaklists Peptide sequences Protein accession numbers ambiguity data size See: Martens and Hermjakob, Molecular BioSystems, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 4. PEPTIDE IDENTIFICATION VALIDATION Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 5. Populations and individuals 10,000 peptide-to-spectrum matches 5% decoy hits Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 6. Eliminating false positives Suspect peptide identifications happen. The problem is that finding them requires detailed analysis of a single spectrum and its identifications, amongst thousands of other spectra… Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 7. Automated interpretation The Netherlands?? Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 8. Manual interpretation Tyrosine phosporylation See: Ghesquière and Helsens, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 9. Peptizer expert system Agent c Agent b Agent d Agent a Agent e Vote casts +1 +1 0 -1 +1 Aggregation of the votes Confident Peptide Identifications Suspicious Trusted subset subset See: Helsens et al, Molecular and Cellular Proteomics, 2008 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 10. Peptizer expert system See: Helsens et al, Molecular and Cellular Proteomics, 2008 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 11. Peptizer expert system See: Helsens et al, Molecular and Cellular Proteomics, 2008 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 12. PROTEIN INFERENCE Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 13. Not all peptides are created equal Gene 1a 1b 2 3 4 5 6a 6b Transcripts 1a 1b 2 5 6a 6b 1a 1b 2 3 5 6a 6b 1b 2 3 4 5 6a 6b 1a 1b 2 3 4 5 6a Translations 2 5 2 3 5 Peptides 2 3 4 5 matching all transcripts 2 3 4 5 redundant matching a transcript subset matching exactly 1 translation Intron Exon UTR Exon CDS Peptide Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 14. Sample preparation consequences See: Nesvizhskii AI et al, Molecular and Cellular Proteomics, 2005 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 15. Sample preparation consequences See: Nesvizhskii AI et al, Molecular and Cellular Proteomics, 2005 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 16. Protein inference: a question of conviction peptides a b c d proteins prot X x x Minimal set Occam { prot Y prot Z x x x x peptides a b c d proteins prot X x x Maximal set anti-Occam { prot Y prot Z x x x x peptides a b c d proteins prot X (-) x x Minimal set with maximal annotation { prot Y (+) prot Z (0) x x x x true Occam? See: Martens and Hermjakob, Molecular BioSystems, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 17. ALGORITHMS FOR THE PROTEIN INFERENCE PROBLEM Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 18. A few algorithms for protein inference • IDPicker Zhang et al, Journal of Proteome Research, 2007 • ProteinProphet Nesvizhskii AI et al, Analytical Chemistry, 2003 • DBToolkit Martens et al, Bioinformatics, 2005 http://genesis.UGent.be/dbtoolkit Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 19. IDPicker parsimonious protein assembly (I) Initialize See: Zhang et al, Journal of Proteome Research, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 20. IDPicker parsimonious protein assembly (II) Collapse See: Zhang et al, Journal of Proteome Research, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 21. IDPicker parsimonious protein assembly (III) Separate See: Zhang et al, Journal of Proteome Research, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 22. IDPicker parsimonious protein assembly (IV) Reduce See: Zhang et al, Journal of Proteome Research, 2007 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 23. ProteinProphet: the simplified view peptide peptide protein probability weight probability peptide probability In iteration 1, all weights w start off as 1/n, with n the degeneracy count for the peptide See: Nesvizhskii AI et al., Analytical Chemistry, 2003 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 24. DBToolkit protein inference peptides a b cd proteins prot X (-) x x Minimal set with maximal annotation { prot Y (+) prot Z (0) x x x x Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 25. Some indications from the HUPO BPP peptides a b c d proteins prot X (-) x x prot Y (+) x prot Z (0) x x x Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 26. PROTEIN INFERENCE AND QUANTIFICATION Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 27. Some inference examples (i) http://genesis.ugent.be/rover/ Nice and easy, 1/1, only unique peptides (blue) and a narrow distribution See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 28. Some inference examples (ii) http://genesis.ugent.be/rover/ Nice and easy, down-regulated See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 29. Some inference examples (iii) http://genesis.ugent.be/rover/ A little less easy, up-regulated See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 30. Some inference examples (iv) http://genesis.ugent.be/rover/ A nice example of the mess of degenerate peptides See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 31. Some inference examples (v) http://genesis.ugent.be/rover/ A bit of chaos, but a defined core distribution See: Colaert et al, Proteomics, 2010 Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011
  • 32. Thank you! Questions? Kenny Helsens BITS MS Data Processing – Protein Inference kenny.helsens@UGent.be UGent, Gent, Belgium – 16 December 2011