Proteomics Informatics (BMSC-GA 4437) Instructor David Feny - PowerPoint PPT Presentation

Proteomics Informatics (BMSC-GA 4437) Instructor David Fenyö Contact information David@FenyoLab.org http://fenyolab.org/presentations/Proteomics_Informatics_2013/

Proteomics Informatics – Learning Objectives Be able analyze a proteomics data set and understand the limitations of the results.

Proteomics Informatics – Overview of Proteomics (Week 1) • Why proteomics? • Bioinformatics • Overview of the course

Motivating Example: Protein Regulation Geiger et al., “Proteomic changes resulting from gene copy number variations in cancer cells”, PLoS Genet. 2010 Sep 2;6(9). pii: e1001090.

Motivating Example: Protein Complexes Alber et al., Nature 2007

Motivating Example: Signaling Choudhary & Mann, Nature Reviews Molecular Cell Biology 2010

Bioinformatics Biological System Experimental Design Samples Measurements Raw Data Data Analysis Information

Mass Spectrometry Based Proteomics Lysis Fractionation Digestion Mass spectrometry Peak Finding Charge determination MS De-isotoping Integrating Peaks Searching Identified and Quantified Proteins

Proteomics Informatics – Overview of Mass spectrometry (Week 2) Ion Mass Detector Source Analyzer intensity mass/charge

Proteomics Informatics – Overview of Mass spectrometry (Week 2) Mass Frag- Mass Ion Source Detector Analyzer 1 mentation Analyzer 2 y b

Proteomics Informatics – Overview of Mass spectrometry (Week 2) LC Ion Source Mass Frag- Mass Analyzer 1 mentation Analyzer 2 Detector intensity intensity intensity intensity intensity intensity intensity intensity intensity intensity intensity intensity intensity intensity intensity mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge mass/charge Time

Proteomics Informatics – Analysis of mass spectra: signal processing, peak finding, and isotope clusters (Week 3) Intensity m/z

Proteomics Informatics – Protein identification I: searching protein sequence collections and significance testing (Week 4) Sequence DB Lysis Pick Protein Fractionation Repeat for all proteins Digestion LC-MS Pick Peptide all peptides Repeat for MS/MS All Fragment Masses MS/MS Compare, Score, Test Significance

Proteomics Informatics – Protein identification II: search engines and protein sequence databases (Week 5)

Proteomics Informatics – Protein identification III: de novo sequencing (Week 6) Amino acid masses 762 100 1-letter 3-letter Chemical Monois Average % Relative Abundance code code formula otopic 875 A Ala 71.0371 71.0788 C 3 H 5 ON R Arg 156.101 156.188 [M+2H] 2+ C 6 H 12 ON 4 N Asn 114.043 114.104 C 4 H 6 O 2 N 2 633 D Asp 115.027 115.089 292 C 4 H 5 O 3 N 405 534 1022 260 389 C Cys 103.009 103.139 C 3 H 5 ONS 504 907 1020 663 778 1080 E Glu 129.043 129.116 C 5 H 7 O 3 N 0 Q Gln 128.059 128.131 C 5 H 8 O 2 N 2 250 500 750 1000 m/z G Gly 57.0215 57.0519 C 2 H 3 ON H His 137.059 137.141 C 6 H 7 ON 3 I Ile 113.084 113.159 C 6 H 11 ON Mass Differences L Leu 113.084 113.159 C 6 H 11 ON K Lys 128.095 128.174 C 6 H 12 ON 2 M Met 131.04 131.193 C 5 H 9 ONS Sequences F Phe 147.068 147.177 C 9 H 9 ON P Pro 97.0528 97.1167 C 5 H 7 ON consistent S Ser 87.032 87.0782 C 3 H 5 O 2 N with spectrum T Thr 101.048 101.105 C 4 H 7 O 2 N W Trp 186.079 186.213 C 11 H 10 ON 2 Y Tyr 163.063 163.176 C 9 H 9 O 2 N V Val 99.0684 99.1326 C 5 H 9 ON

Proteomics Informatics – Protein identification IV: spectrum library searching (Week 7) Spectrum Library Lysis Pick Fractionation Spectrum all spectra Repeat for Digestion LC-MS/MS MS/MS Compare, Score, Test Significance Identified Proteins

Proteomics Informatics – Protein quantitation I: metabolic labeling (SILAC), chemical labeling, label-free quantitation, spectrum counting (Week 8) Sample i C ij Protein j Lysis p L Peptide k p ij Pr Fractionation p MS ij p ik D Digestion ijk p Pep MS  k I ik LC-MS ik p LC ik     L Pr D Pep LC MS  p p p p p p C I     ik k ij ij ij ijk ik ik ik j I k  ik C  L Pr D Pep LC MS ij p p p p p p k ij ij ijk ik ik ik

Proteomics Informatics – Protein quantitation I: metabolic labeling (SILAC), chemical labeling, label-free quantitation, spectrum counting (Week 8) Sample i Protein j Peptide k Lysis Assumption:  L Pr D Pep LC MS p p p p p p Fractionation k ij ij ijk ik ik ik constant for all samples Digestion  C / C I / I i i i i j j j j LC-MS n m n m MS MS

Proteomics Informatics – Protein quantitation II: software (Week 9) Skyline MaxQuant

Proteomics Informatics – Protein characterization I: post-translational modifications (Week 10) Peptide with two possible modification sites Matching MS/MS spectrum Intensity m/z Which assignment does the data support? 1, 1 or 2, or 1 and 2?

Proteomics Informatics – Protein Characterization II: protein-protein interactions, cross-linking, top-down, non-covalent complexes (Week 11) A D A C B Protein identification

Proteomics Informatics – Molecular Signatures (Week 12)

Proteomics Informatics – Molecular Signatures (Week 12)

Proteomics Informatics – Presentations of projects (Week 13) Select a published data set that has been made public and reanalyze it. Highlighted data sets: http://www.thegpm.org/ 10 min presentations

Proteomics Informatics (BMSC-GA 4437) Instructor David Fenyö Contact information David@FenyoLab.org http://fenyolab.org/presentations/Proteomics_Informatics_2013/