Wikiomics:Protein mass spectrometry

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*** MEGGAYGAGKAGGAFDPYTL => FYAGADEAGMGTYKGGAGLP (used [http://host9.bioinfo3.ifom-ieo-campus.it/sms2/shuffle_protein.html SMS],  results differ each time) -> recommended by EBI ppl
*** MEGGAYGAGKAGGAFDPYTL => FYAGADEAGMGTYKGGAGLP (used [http://host9.bioinfo3.ifom-ieo-campus.it/sms2/shuffle_protein.html SMS],  results differ each time) -> recommended by EBI ppl
** random (i.e. creating database of random proteins based on frequency of AA in source fasta file)
** random (i.e. creating database of random proteins based on frequency of AA in source fasta file)
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* to create decoy database use [http://genesis.ugent.be/dbtoolkit/ DBToolkit] free java standalone
* to create decoy database use [http://genesis.ugent.be/dbtoolkit/ DBToolkit] free java standalone
 +
* experimental: [http://www.cs.usu.edu/~mjiang/ushuffle/ uShuffle] "generating uniform random permutations of biological sequencest hat preserve the exact k-let counts" i.e dipeptides
===Modification databases===
===Modification databases===
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== $$$ programs ==
== $$$ programs ==
* [http://www.waters.com/WatersDivision/contentd.asp?watersit=RHEY-5LHBSW ProteinLynx Global SERVER] $$$, from Waters Waters Corporation
* [http://www.waters.com/WatersDivision/contentd.asp?watersit=RHEY-5LHBSW ProteinLynx Global SERVER] $$$, from Waters Waters Corporation
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* [http://phenyx.vital-it.ch/pwi/ Phenyx] from GeneBio (online web server)  
+
* [http://phenyx.vital-it.ch/pwi/ Phenyx] from GeneBio (online web server)
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* [http://www.bioinquire.com ProteoIQ] from BIOINQUIRE
==Other==
==Other==
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* [[User:Darked|Darek Kedra]] wrote this tutorial
* [[User:Darked|Darek Kedra]] wrote this tutorial
<!-- other contributors, put yourself here -->
<!-- other contributors, put yourself here -->
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[[Category:Protocol]]
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[[Category:In silico]]
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[[Category:Data analysis]]

Current revision

Protein mass spectrometry can be divided into:

  • identification of proteins/peptides
  • quantification

A good introductory tutorial from USC Computational Biology group is here.

Contents

Protein/peptide identification

Peptide Mass Fingerprinting (PMF) or (MS)

Old method, superseded by MS/MS

  • algorithms:
  • caveats
    • no sequence information
    • journals started to require that at least one peptide of a protein identified by PMF should be confirmed by MS/MS

Peptide fragment fingerprinting (PFF) or (MS/MS)

  • algorithms (other/new/experimental):
    • Spectrum Mill $$$
    • MASPIC
      • this paper claims 5-15% more confident hits than Sequest: [1]
    • InsPecT A new variable mods search from Pevzner & Tanner @UCSD (free?)
  • filtering of the results
    • Trans Proteomic Pipeline [2] (free?)
    • DTASelect it seems to be in a semi-frozen state (free for nonprofit but requires signed MTA)

Databases

Protein databases

Use (if possible):

  • IPI International Protein Index
  • always use target-decoy database (i.e. concatenated: human_IPI + reversed_human_IPI)
  • decoy databases creation methods:
    • protein reversal (simple to perform. does not scramble fortunately quite rare palindromic sequences)
      • MEGGAYGAGKAGGAFDPYTL -=> LTYPDFAGGAKGAGYAGGEM
    • peptide pseudo-reversal (used in Sorcerer by Sage-N Research)
      • MEGGAYGAGKAGGAFDPYTL => (trypsin digest, used Ms-Digest) MEGGAYGAGK AGGAFDPYTL => GAGYAGGEMK-ALTYPDFAGG (each peptide reversed, but trypsin digestion site preserved -> guessed from the Elias 2007 paper)
    • shuffled
      • MEGGAYGAGKAGGAFDPYTL => FYAGADEAGMGTYKGGAGLP (used SMS, results differ each time) -> recommended by EBI ppl
    • random (i.e. creating database of random proteins based on frequency of AA in source fasta file)
  • to create decoy database use DBToolkit free java standalone
  • experimental: uShuffle "generating uniform random permutations of biological sequencest hat preserve the exact k-let counts" i.e dipeptides

Modification databases

  • Unimod (> 500 natural + labels)
  • Delta Mass A Database of Protein Post Translational Modifications (in vivo)
  • RESID detailed descriptions of > 400 modifications


Peptide Tag Searching

"Designed to characterize peptides with mutations or unexpected post-translational modifications." (from Popitam page)

  • GutenTag free for non-profit, MTA required. Assigns fewer peptides than Sequest but with fewer false positives. Occupies a middle ground between mainstream search algorithms and de novo sequencing.

de novo sequence determination algorithms

For comparison see: Performance Evaluation of Existing De Novo Sequencing Algorithms by Pevtsov et al. (2006) PDF

Comonly used

Novel

These (according to authors) are better than any of the four listed above

  • NovoHMM for non-commercial use only, Windows binary. Model file for ThermoFinnigan LCQ mass spectrometer.

According to Pevtsov NovoHMM has the best sensitivity, and its performance is the best in the entire range of spectrum quality. But the overall performance of NovoHMM is not significantly different from the performance of PEAKS and PepNovo.

  • NovoHMM++

Spectral matching

The idea is that if one can match spectrum of an unknown peptide to a very similar MS/MS spectrum in a database with a determined sequence/annotation then one can annotate unknown peptide in a process similar to orthologue annotation in protein sequence databases. Caveat: bad annotations will also get propagated.

  • P3 (server) from Global Proteomics Machine (free)
  • SpectraST from ISB, Seattle (not as many species/options as P3). Ca 500x faster than Sequest on the same set.

lecture notes by Henry Lam from ISB

  • BiblioSpec from MacCoss lab. (free for non-profit, online licence)
    • command line only

Spectral libraries available here@PeptideAtlas

Protein quantification

  • approaches
    • isotopic labeling (ICAT, ITRAQ, SILAC, 18O- or 15N-labeling)
    • label-free methods
    • COFRADIC
  • software
    • ASAPRatio from Trans Proteomics Pipeline:
      "calculates the relative abundances of proteins and the corresponding confidence intervals from ICAT-type ESI-LC/MS data"
    • MSQuant Parser for Mascot results for quantitation (Windows only)
    • MapQuant Suite

Frameworks/pipelines

  • Trans Proteomic Pipeline (TPP) most popular, included in Sorcerer from Sage-N Research. Windows/Cygwin/Perl or Linux based.
  • Open-MS German, C++ based
  • Sorcerer $$$ FPGA-based fast hardware solution for SEQUEST & Tandem searches with TPP on top of it.

File formats

  • .bdx from Bruker Daltonics
  • .dta SEQUEST/Thermoelectron. Two versions:
    • single pectra
    • multiple spectra concateneted in one file
  • .mgf multiple spectra, Mascot (Matrix science)
  • mzXML (used by Trans Proteomics Pipeline)
  • mzData (standard set by HUPO Proteomics Standard Initiative)

Spectrum datasets

Good for testing programs:

Web sites

Reviews

For a good review of programs and aspects of protein identification by mass spectrometry see:


Tutorials

Other tools to be sorted out

Ortholog searches using sequence tags/ambigous sequence

$$$ programs

Other

Needs to be sorted out.

Experimental

  • DeNovoID web
  • SPIDER (PDF) de novo + homology search in other species based on a set of tags
  • OpenSea (HTML) Java program available from authors
ModifiComb (HTML) (available from authors?)
  • MODi web server for PTMs discovery
  • SILVER view your spectra with LOD scores



Biblio

  1. Brun, Virginie, Alain Dupuis, Annie Adrait, Marlene Marcellin, Damien Thomas, Magali Court, et al. “Isotope-labeled Protein Standards: Toward Absolute Quantitative Proteomics.” Mol Cell Proteomics 6, no. 12 (December 1, 2007): 2139-2149.
  2. Carr, Steven, Ruedi Aebersold, Michael Baldwin, Al Burlingame, Karl Clauser, and Alexey Nesvizhskii. “The Need for Guidelines in Publication of Peptide and Protein Identification Data: Working Group On Publication Guidelines For Peptide And Protein Identification Data.” Mol Cell Proteomics 3, no. 6 (June 1, 2004): 531-533.
  3. Craig, Robertson, and Ronald C. Beavis. “TANDEM: matching proteins with tandem mass spectra.” Bioinformatics 20, no. 9 (June 12, 2004): 1466-1467.
  4. Domon, Bruno, and Ruedi Aebersold. “Mass Spectrometry and Protein Analysis.” Science 312, no. 5771 (April 14, 2006): 212-217.
  5. Elias, Joshua E, and Steven P Gygi. “Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.” Nat Meth 4, no. 3 (March 2007): 207-214.
  6. Elias, Joshua E, Wilhelm Haas, Brendan K Faherty, and Steven P Gygi. “Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations.” Nat Meth 2, no. 9 (2005): 667-675.
  7. Eng, Jimmy K., Ashley L. McCormack, and John R. Yates. “An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.” Journal of the American Society for Mass Spectrometry 5, no. 11 (November 1994): 976-989.
  8. Kersey, Paul J., Jorge Duarte, Allyson Williams, Youla Karavidopoulou, Ewan Birney, and Rolf Apweiler. “The International Protein Index: An integrated database for proteomics experiments.” PROTEOMICS 4, no. 7 (2004): 1985-1988.
  9. Kim, Sangtae, Seungjin Na, Ji Woong Sim, Heejin Park, Jaeho Jeong, Hokeun Kim, et al. “MODi : a powerful and convenient web server for identifying multiple post-translational peptide modifications from tandem mass spectra.” Nucl. Acids Res. 34, no. suppl_2 (July 1, 2006): W258-263.
  10. Martens, Lennart, Sandra Orchard, Rolf Apweiler, and Henning Hermjakob. “Human Proteome Organization Proteomics Standards Initiative: Data Standardization, a View on Developments and Policy.” Mol Cell Proteomics 6, no. 9 (September 1, 2007): 1666-1667.
  11. Perkins, David N., Darryl J. C. Pappin, David M. Creasy, and John S. Cottrell. “Probability-based protein identification by searching sequence databases using mass spectrometry data.” Electrophoresis 20, no. 18 (1999): 3551-3567.
  12. Roos, Franz F., Riko Jacob, Jonas Grossmann, Bernd Fischer, Joachim M. Buhmann, Wilhelm Gruissem, et al. “PepSplice: cache-efficient search algorithms for comprehensive identification of tandem mass spectra.” Bioinformatics 23, no. 22 (November 15, 2007): 3016-3023.
  13. Webb-Robertson, Bobbie-Jo M., and William R. Cannon. “Current trends in computational inference from mass spectrometry-based proteomics.” Brief Bioinform 8, no. 5 (September 1, 2007): 304-317.


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