PTM Identification and Localization from MS Proteomics Data

Transcription

1 PTM Identification and Localization from MS Proteomics Data Marc Vaudel Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway

2 Intensity Intensity Proteomics Bioinformatics Proteins Peptides Preparation LC-MS/MS MS1 Spectra + MS2 Spectra Retention Time Proteomics Dataset Precursor m/z 2 Verheggen et al., Mass Spectrom. Rev., 2017

3 Intensity Peptide Identification Targeted (Discovery): Find known mass fingerprints Spectra Library + Precursor m/z 3 Peptide specific intensity

4 Intensity Peptide Identification Untargeted (Discovery): Compare spectra to expected content of samples Spectra Proteins + Precursor m/z 4 Best matching peptide

5 Peptide Identification Database Search Engines Spectrum Sequencing Spectral Libraries

6 There is PTM and PTM One pipeline fits all

7 Database Search Engines Search Parameters Peak Lists Pre-processing Matching Scoring PSMs Database In silico digestion In silico fragmentation >sw Q15149 PLEC_HUMAN Plectin OS=Homo sapiens GN=PLEC PE=1 SV=3 MVAGMLMPRDQLRAIYEVLFREGVMVAKKDRRPRSLHPHVPGVTNLQVMRAMASL RARGLVRETFAWCHFYWYLTNEGIAHLRQYLHLPPEIVPASLQRVRRPVAMVMPAR RTPHVQAVQGPLGSPPKRGPLPTEEQRVYRRKELEEVSPETPVVPATTQRTLARP GPEPAPATDERDRVQKKTFTKWVNKHLIKAQRHISDLYEDLRDGHNLISLLEVLSGD SLPREKGRMRFHKLQNVQIALDYLRHRQVKLVNIRNDDIADGNPKLTLGLIWTIILHF QISDIQVSGQSEDMTAKEKLLLWSQRMVEGYQGLRCDNFTSSWRDGRLFNAIIHRH KPLLIDMNKVYRQTNLENLDQAFSVAERDLGVTRLLDPEDVDVPQPDEKSIITYVSS LYDAMPRVPDVQDGVRANELQLRWQEYRELVLLLL Search Engine

8 Number of Yearly Citations Verheggen et al., Mass Spectrom. Rev., 2017 Database Search Engines Other 1000 MyriMatch InsPecT 800 Andromeda 600 OMSSA X! Tandem 400 Paragon 200 SEQUEST Mascot Year

9 Database Search Engines Search Parameters Chimeric Spectra Peak Lists Pre-processing Matching Scoring PSMs Database In silico digestion In silico fragmentation Multiple Pass Search Search Engine

10 See Nesvizhskii, Journal of Proteomics, 2010 Peptide score distribution 100% 80% 60% 40% 20% 0% score

11 The search space problem Verheggen et al., Mass Spectrom. Rev., 2017

12 See Nesvizhskii, Journal of Proteomics, 2010 Peptide score distribution 100% 80% 60% 40% 20% 0% score

13 Chick et al., Nature Biotechnology, 2015 Which PTMs include in the search? Open search: MSFragger, PMID Parameters optimization: Param-Medic, PMID Limit PTMs per peptide

14 The search space problem Verheggen et al., Mass Spectrom. Rev., 2017

15 Site determining ions ADTSQEICSPR ADTSQEICSPR ADTSQEICSPR ADpTSQEICSPR ADTpSQEICSPR ADTSQEICpSPR ,000 1,200 1,400 m/z

16 Tauss et al., JPR, 2011 Scoring PTM isoforms Noise level Isoform score Significance

17 Tauss et al., JPR, 2011 Scoring PTM isoforms Noise level Isoform score Significance

18 Observed Performance Expected Simplistic fragmentation model Challenges in noise estimation

19 Tauss et al., JPR, 2011 Scoring PTM isoforms Noise level Isoform score Significance

20 Fermin et al., MCP, 2013 False Localization Rate Estimation 100% 80% 60% 40% 20% 0% score

21 What about proteins? Nesvizhskii and Aebersold, MCP, 2005

22 PeptideShaker In practice Scoring integrated in most analysis pipelines

23 There is PTM and PTM One pipeline fits all

24 Simplify to standard problem

25 De novo identification MS1 MS2 MS3

26 De novo identification Vegard Tveit

27 De novo identification Vegard Tveit

28 Hybrid approaches Stadlmann, Taubenschmid, et al., Nature, 2017

29 Take home message Vast space of possibilities Importance of experimental workflow and data quality Error rates are difficult to estimate Complex PTMs remain to be explored Talents wanted!

30 Need more help? proteomics-academy.org -> Conferences -> Courses -> Educational Wiki -> Q & A

31 Acknowledgement

32 02/10/17 Thank you for your attention! Questions?