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Proteomics.  Proteomics directly detects expression of proteins.  Proteome research permits the discovery of new protein markers for diagnostic purposes.

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Presentation on theme: "Proteomics.  Proteomics directly detects expression of proteins.  Proteome research permits the discovery of new protein markers for diagnostic purposes."— Presentation transcript:

1 Proteomics

2  Proteomics directly detects expression of proteins.  Proteome research permits the discovery of new protein markers for diagnostic purposes and of novel molecular targets for drug discovery. Proteomics


4 1. SWISS-2DPAGE database SWISS-2DPAGE is an annotated two- dimensional polyacrylamide gel electrophoresis (2-D PAGE) database established in 1993. The SWISS-2DPAGE database is maintained by the Swiss Institute of Bioinformatics, in collaboration with the Central Clinical Chemistry Laboratory of the Geneva University Hospital.


6 SWISS-2DPAGE Search Page

7 View entry in original SWISS-2DPAGE format Entry name: VSP2_ARATH Primary accession number:82122 Entered in SWISS-2DPAGE inRelease 13, December 2000 Last modified in Release 14, October 2001 Name and origin of the protein: DescriptionVegetative storage protein 2. Gene name(s)VSP2 OR AT5G24770 From Arabidopsis thaliana (Mouse-ear cress). [TaxID: 3702]3702 TaxonomyEukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta; Spermatophyta; Magnoliophyta; eudicotyledons; core eudicots; Rosidae; eurosids II; Brassicales; Brassicaceae; Arabidopsis. References[1] MAPPING ON GEL. Sarazin B., Tonella L., Marques K., Paesano S., Chane-Favre L., Sanchez J.-C., Hochstrasser D.F., Thiellement H.; Submitted (OCT-2000) to the SWISS-2DPAGE database. 2D PAGE maps for identified proteins Compute the theoretical pI/Mw How to interpret a protein mapCompute the theoretical pI/Mw How to interpret a protein map Arabidopsis thaliana MAP LOCATIONS: SPOT 2D-001KKV: pI=6.47, Mw=29849 *** the clicked spot ***2D-001KKV MAPPING: MASS SPECTROMETRY [1].[1]

8 Mass spectrometry (MS)

9 2. PeptIdent PeptIdent is a tool that allows the identification of proteins using pI, MW and peptide mass fingerprinting data. Experimentally measured, user-specified peptide masses are compared with the theoretical peptides calculated for all proteins in the SWISS-PROT/TrEMBL databases.


11 3. Mascot Mascot is a powerful search engine that uses mass spectrometry data to identify proteins from primary sequence databases.


13 Concise Protein Summary Report Switch to full Protein Summary Report To create a bookmark for this report, right click this link: Concise Summary Report (../data/20020713/FATeiic.dat) P82691 Mass: 1011 Total score: 25 Peptides matched: 1 Pyrokinin-1 (Pea-PK-1) (FXPRL-amide)P82691 P82041 Mass: 1736 Total score: 24 Peptides matched: 1 Uperin 3.4 1. 3.P82041 P36396 Mass: 2069 Total score: 23 Peptides matched: 1 Sex- determining region Y protein (Testis-determining factor) (Fragment)P36396


15 4. FindMod This tool examines peptide mass fingerprinting data for mass differences between empirical and theoretical peptides. Where mass differences correspond to a post-translational modification (PTM).

16 Post- translational modification s Mass values used in FindMod ModificationsAbbreviationMonoisotopic Average __ AcetylationACET42.010642.0373Acetylation AmidationAMID-0.9840-0.9847Amidation Beta-methylthiolationBMTH45.987711846.08688Beta-methylthiolation BiotinBIOT226.0776226.2934Biotin CarbamylationCAM43.0058143.02502Carbamylation CitrullinationCITR0.98402760.98476Citrullination C-MannosylationCMAN162.052823162.1424C-Mannosylation DeamidationDEAM0.98400.9847Deamidation N-acyl diglyceride cysteine (tripalmitate)DIAC788.7258789.3202 DimethylationDIMETH28.031428.0538Dimethylation FADFAD783.1415783.542FAD FarnesylationFARN204.1878204.3556Farnesylation FormylationFORM27.994928.0104Formylation Geranyl-geranylGERA272.2504272.4741Geranyl-geranyl Gamma-carboxyglutamic acidGGLU43.9898344.0098Gamma-carboxyglutamic acid O-GlcNAcGLCN203.0794203.1950O-GlcNAc Glucosylation (Glycation)GLUC162.0528162.1424Glucosylation (Glycation) HydroxylationHYDR15.994915.9994Hydroxylation LipoylLIPY188.033188.3027Lipoyl MethylationMETH14.015714.0269Methylation MyristoylationMYRI210.1984210.3598Myristoylation PalmitoylationPALM238.2297238.4136Palmitoylation PhosphorylationPHOS79.966379.9799Phosphorylation Pyridoxal phosphatePLP229.014229.129Pyridoxal phosphate PhosphopantetheinePPAN339.078339.3234Phosphopantetheine Pyrrolidone carboxylic acidPYRR-17.0266-17.0306Pyrrolidone carboxylic acid SulfationSULF79.956880.0642Sulfation TrimethylationTRIMETH42.047142.0807Trimethylation



19 Biochemical Pathway Databases Linking the biochemical pathways together and integration with the genomic data are the great tasks of biochemical pathway databases.

20 Metabolomics: From Genes to Pathways:






26 Where do we go? “Deconstruction of biological processes into their molecular components”.

27 DNA (Genomics) RNA (Transcriptomics) Protein (Proteomics) Metabolites (Metabolomics)

28 From: Gene, genome, cell, organism, population,… toward System Biology

29 What are we going to do?

30 Fact: Individual research units would not work any more! Recommendation: Team up! Go beyond your own, your institute, and your country boundaries.

31 Fact: Genomic data are suppose to reduce time and efforts for preparation of reagents, resources and information. Recommendation: Think big! Search and use data intelligently. Turn attention to complex biology from various angles, i.e. have all needed specialty in your team.

32 Fact: A mass of data is available freely! Recommendation: Learn how to use! Make use of them to develop technologies.

33 Fact: Biology world is rapidly changing! Recommendation: Keep up with changes! Re-establish systems with more flexibility and more freedom. Loose regulations for funding, employment, etc. Re-design your research project.

34 Thanks for Your Attention

35 Cautions: One protein with different roles: Alpha-enolase in liver T-crystallin in eye lens One structure in proteins with diverse functions: TIM barrels in isomerases, oxidoreductase and hydrolases. 30% error in automated annotations.

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