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Proteomics

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Proteome research permits the discovery of new protein markers ... L., Marques K., Paesano S., Chane-Favre L., Sanchez J.-C., Hochstrasser D.F., Thiellement H. ... – PowerPoint PPT presentation

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Title: Proteomics


1
Proteomics
2
Proteomics
  • 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.

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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.

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SWISS-2DPAGE Search Page
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  • View entry in original SWISS-2DPAGE format
  • Entry name VSP2_ARATH
  • Primary accession number82122
  • 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
  • TaxonomyEukaryota Viridiplantae Streptophyta
    Embryophyta Tracheophyta Spermatophyta
    Magnoliophyta eudicotyledons core eudicots
    Rosidae eurosids II Brassicales Brassicaceae
    Arabidopsis.
  • References1  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 map
  • Arabidopsis thaliana MAP LOCATIONS SPOT
    2D-001KKV pI6.47, Mw29849 the clicked spot
  • MAPPING MASS SPECTROMETRY 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.

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

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  • 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)
  • P82041 Mass 1736 Total score 24 Peptides
    matched 1 Uperin 3.4 1. 3.
  • P36396 Mass 2069 Total score 23
    Peptides matched 1 Sex-determining region Y
    protein (Testis-determining factor) (Fragment)

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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 modifications Mass values used
in FindMod
  • Modifications Abbreviation Monoisotopic Average
    __
  • Acetylation ACET 42.0106 42.0373
  • Amidation AMID -0.9840 -0.9847
  • Beta-methylthiolation BMTH 45.9877118 46.08688
  • Biotin BIOT 226.0776 226.2934
  • Carbamylation CAM 43.00581 43.02502
  • Citrullination CITR 0.9840276 0.98476
  • C-Mannosylation CMAN 162.052823 162.1424
  • Deamidation DEAM 0.9840 0.9847
  • N-acyl diglyceride
  • cysteine (tripalmitate) DIAC 788.7258 789.3202
  • Dimethylation DIMETH 28.0314 28.0538
  • FAD FAD 783.1415 783.542
  • Farnesylation FARN 204.1878 204.3556
  • Formylation FORM 27.9949 28.0104
  • Geranyl-geranyl GERA 272.2504 272.4741
  • Gamma-carboxyglutamic acid GGLU 43.98983 44.0098
  • O-GlcNAc GLCN 203.0794 203.1950

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

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Metabolomics From Genes to Pathways
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Where do we go?
  • Deconstruction of biological processes into
    their molecular components.

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DNA (Genomics)
RNA (Transcriptomics)
Protein (Proteomics)
Metabolites (Metabolomics)
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From Gene, genome, cell, organism, population,…
toward System Biology
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What are we going to do?
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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.

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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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