Proteomics Informatics (BMSC-GA 4437) Instructor David Fenyö Contact information David@FenyoLab.org htt://fenyolab.org/resentations/proteomics_informatics_2013/
Learning Objectives Be able analyze a roteomics data set and understand the limitations of the results.
Overview of Proteomics (Week 1) Why roteomics? Bioinformatics Overview of the course
Motivating Examle: Protein Regulation Geiger et al., Proteomic changes resulting from gene coy number variations in cancer cells, PLoS Genet. 2010 Se 2;6(9). ii: e1001090.
Motivating Examle: Protein Comlexes Alber et al., Nature 2007
Motivating Examle: Signaling Choudhary & Mann, Nature Reviews Molecular Cell Biology 2010
Bioinformatics Biological System Exerimental Design Samles Measurements Raw Data Data Analysis Information
Mass Sectrometry Based Proteomics Lysis Fractionation Digestion Mass sectrometry MS Peak Finding Charge determination De-isotoing Integrating Peaks Searching Identified and Quantified Proteins
Overview of Mass sectrometry (Week 2) Ion Source Mass Analyzer Detector mass/charge
Overview of Mass sectrometry (Week 2) Ion Source Mass Analyzer 1 Fragmentation Mass Analyzer 2 Detector b y
Proteomics Informatics Overview of Mass sectrometry (Week 2) Ion Source LC Mass Analyzer 1 Fragmentation Mass Analyzer 2 Detector 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
Analysis of mass sectra: signal rocessing, eak finding, and isotoe clusters (Week 3) Intensity m/z
Protein identification I: searching rotein sequence collections and significance testing (Week 4) Sequence DB Lysis Fractionation LC-MS MS/MS MS/MS Digestion Pick Protein Pick Petide All Fragment Masses Comare, Score, Test Significance Reeat for all etides Reeat for all roteins
Protein identification II: search engines and rotein sequence databases (Week 5)
% Relative Abundance Amino acid masses 1-letter 3-letter Chemical Monois Average code code formula otoic A Ala C 3 H 5 ON 71.0371 71.0788 R Arg C 6 H 12 ON 4 156.101 156.188 N Asn C 4 H 6 O 2 N 2 114.043 114.104 D As C 4 H 5 O 3 N 115.027 115.089 C Cys C 3 H 5 ONS 103.009 103.139 E Glu C 5 H 7 O 3 N 129.043 129.116 Q Gln C 5 H 8 O 2 N 2 128.059 128.131 G Gly C 2 H 3 ON 57.0215 57.0519 H His C 6 H 7 ON 3 137.059 137.141 I Ile C 6 H 11 ON 113.084 113.159 L Leu C 6 H 11 ON 113.084 113.159 K Lys C 6 H 12 ON 2 128.095 128.174 M Met C 5 H 9 ONS 131.04 131.193 F Phe C 9 H 9 ON 147.068 147.177 P Pro C 5 H 7 ON 97.0528 97.1167 S Ser C 3 H 5 O 2 N 87.032 87.0782 T Thr C 4 H 7 O 2 N 101.048 101.105 W Tr C 11 H 10 ON 2 186.079 186.213 Y Tyr C 9 H 9 O 2 N 163.063 163.176 V Val C 5 H 9 ON 99.0684 99.1326 Proteomics Informatics Protein identification III: de novo sequencing (Week 6) 100 0 292 405 260 389 534 504 [M+2H] 2+ 633 762 875 1022 663 778 9071020 1080 250 500 750 1000 m/z Sequences consistent with sectrum Mass Differences
Protein identification IV: sectrum library searching (Week 7) Identified Proteins Lysis Fractionation Digestion LC-MS/MS MS/MS Sectrum Library Pick Sectrum Reeat for all sectra Comare, Score, Test Significance
Protein quantitation I: metabolic labeling (SILAC), chemical labeling, label-free quantitation, sectrum counting (Week 8) C ij L ij D ijk LC ik I ij Pr Pe ik C Lysis Fractionation Digestion LC-MS ik k ij ij ij ijk j C k ij k L ij L Pr ij Pr I D ijk ik D Pe ik Pe ik LC ik MS ik I ik Samle i Protein j Petide k LC ik MS ik MS ik MS k
Protein quantitation I: metabolic labeling (SILAC), chemical labeling, label-free quantitation, sectrum counting (Week 8) Lysis Fractionation Digestion LC-MS Assumtion: k L ij Pr ij D ijk Pe ik LC constant for all samles C i j/ C j I j/ n m n i Samle i Protein j Petide k i ik I i m j MS ik MS MS
Protein quantitation II: software (Week 9) Skyline MaxQuant
Protein characterization I: ost-translational modifications (Week 10) Petide with two ossible modification sites Intensity Matching MS/MS sectrum m/z Which assignment does the data suort? 1, 1 or 2, or 1 and 2?
Protein Characterization II: rotein-rotein interactions, cross-linking, to-down, non-covalent comlexes (Week 11) A D A C B Protein identification
Molecular Signatures (Week 12)
Molecular Signatures (Week 12)
Presentations of rojects (Week 13) Select a ublished data set that has been made ublic and reanalyze it. Highlighted data sets: htt://www.thegm.org/ 10 min resentations
Proteomics Informatics (BMSC-GA 4437) Instructor David Fenyö Contact information David@FenyoLab.org htt://fenyolab.org/resentations/proteomics_informatics_2013/