EBI is an Outstation of the European Molecular Biology Laboratory. MS Identification Dr. Juan...

EBI is an Outstation of the European Molecular Biology Laboratory.

MS Identification

Dr. Juan Antonio VIZCAINO

PRIDE Group coordinatorPRIDE team, Proteomics Services Group

PANDA group

European Bioinformatics Institute

Hinxton, Cambridge

United Kingdom

EBI Bulgaria RoadshowRotterdam, 12 June 2012

Juan A. Vizcaí[email protected]

Overview …

• Search engines: peptide identification

• Protein inference

• De novo and spectral searches

• Choosing the right protein sequence DB

• You need to learn many things…



It should not be a black box…

From: Lilley et al., Proteomics, 2011



MS proteomics: Shot-gun/bottom-up approaches

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MS/MS analysis

fragmentation

PROTOCOL

peptides

proteins

sequencedatabase



PMF IDENTIFICATION



Peptide Mass Fingerprinting (MS)

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MS analysis

Peptide MassFingerprinting

(PMF)MW

- Each peak in the spectrum represents a peptide (or mixture of peptides)

- Information about the Mass and Charge

Not very used at present except forGel Based approaches

(in this case the Molecular Weight of the protein is known)



Peptide Mass Fingerprinting (MS) in the webAldente (Phenyx): http://www.expasy.org/tools/aldente/

ASCQ_ME: https://www.genopole-lille.fr/logiciel/ascq_me/

Bupid: http://zlab.bu.edu/Amemee/

Mascot: http://www.matrixscience.com/search_form_select.html

MassSearch: http://www.cbrg.ethz.ch/services/MassSearch

MS-Fit (Protein Prospector):http://prospector.ucsf.edu/prospector/mshome.htm

PepMAPPER: http://www.nwsr.manchester.ac.uk/mapper/

Profound (Prowl): http://prowl.rockefeller.edu/prowl-cgi/profound.exe

XProteo: http://xproteo.com:2698/

http://www.expasy.org/tools/aldente/

https://www.genopole-lille.fr/logiciel/ascq_me/

http://zlab.bu.edu/Amemee/

http://www.matrixscience.com/search_form_select.html

http://www.cbrg.ethz.ch/services/MassSearch

http://prospector.ucsf.edu/prospector/mshome.htm

http://www.nwsr.manchester.ac.uk/mapper/

http://prowl.rockefeller.edu/prowl-cgi/profound.exe

http://xproteo.com:2698/



MS/MS IDENTIFICATION



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MS analysis

Peptide MassFingerprinting

(PMF)

MS/MS

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Peptide sequence information

(on top of Mass and Charge)

Fragmentation



Protein database based comparison

Sequential comparison: de novo approaches

Spectral comparison

database sequence theoreticalspectrum

experimentalspectrum

compare

database sequence experimentalspectrum

compare de novosequence

Spectrallibrary



compare

Modified From: Eidhammer, Flikka, Martens, Mikalsen – Wiley 2007

Three types of MS/MS identification



MS proteomics: peptide IDs and protein IDs

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MS/MS spectra

proteins




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MS/MS spectra

proteins




proteins

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MS/MS spectra

peptides

Searchengine

sequencedatabase

UniProtIPI

RefSeq

TDMDNQIVVSDYAQMDR

LFDQAFGLPRAKPLMELIER

DESTNVDMSLAQRDIVVQETMEDIDK

NGMFFSTYDRGTAGNALMDGASQL



SEARCH ENGINES



Search engines

Sequence database matching

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Experimental Spectra

Proteins

Peptides

Spectra

TDMDNQIVVSDYAQMDRLFDQAFGLPRAKPLMELIER



VDMSLAQRDIVVQETMEDIDK

…

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Theoretical Spectra

UniProtIPI

RefSeq

sequencedatabase



Search engines

How good is the correlation?- Scores are generated by search engines- Usually the best match is kept

Theoretical Spectra

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Experimental Spectra

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Search engines

Taken from Nesvizhskii, J Proteomics, 2010



Search engines




• MASCOT (Matrix Science)http://www.matrixscience.com

• SEQUEST (Scripps, Thermo Fisher Scientific)http://fields.scripps.edu/sequest

• X!Tandem (The Global Proteome Machine Organization)http://www.thegpm.org/TANDEM

• OMSSA (NCBI)http://pubchem.ncbi.nlm.nih.gov/omssa/

The most popular algorithms



Incorrect identifications

Correctidentifications

False positivesFalse negatives

Threshold score

Adapted from: www.proteomesoftware.com – Wiki pages

Overall concept of scores and cut-offs



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p=0.05 p=0.01 p=0.005 p=0.0005

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false positives

identifications

higher stringency

Playing with probabilistic cut-off scores



• Very well established search engine

• Can be used for MS/MS (PFF) identifications

• Based on a cross-correlation score (includes experimental peak height)

• Published core algorithm (patented, licensed to Thermo Fisher Scientific)

• Provides preliminary (Sp) score, rank, cross-correlation score (XCorr),

and score difference between the top tow ranks (deltaCn, Cn)

• Thresholding is up to the user, and is commonly done per charge state

• Many extensions exist to perform a more automatic validation of results

SEQUEST

CrossCorr

avg AutoCorr offset=-75 to 75 XCorr =

deltaCn=

XCorr1 XCorr 2

XCorr1



Search engines: Sequest

The XCorr is high if the direct comparison is significantly greater than

the background

It measures how good the XCorr is relative to the next best match.



• Very well established search engine

• Can do MS (PMF) and MS/MS (PFF) identifications

• Based on the MOWSE score

• Unpublished core algorithm (trade secret)

• Predicts an a priori threshold score that identifications need to pass

• From version 2.2, Mascot allows integrated decoy searches

• Provides rank, score, threshold and expectation value per identification

• Customizable confidence level for the threshold score

Search engines: Mascot



Search engines: Mascot

www.matrixscience.com



Search engines: X!Tandem

• Open source search engine• Can be used for MS/MS experiments• Based on a hyperscore, than only takes into account

b and y ions.• Published core algorithm and it is freely available• Fast and able to handle PTMs in an iterative fashion• Used as an auxiliary search engine

by-Score= Sum of intensities of peaks matchingB-type or Y-type ions

HyperScore= by-Score Ny! Nb!



Search engines: OMSSA

• Open source search engine• Can be used for MS/MS experiments• Relies on a Poisson distribution• Published core algorithm and it is freely available• Provides an expectancy score, similar to the BLAST

E-value• Very good performance in comparison with the

others• Used as an auxiliary search engine




proteins

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MS/MS spectra

peptides

Searchengine

sequencedatabase

UniProtIPI

RefSeq

TDMDNQIVVSDYAQMDR




So far, we have actually identified peptides, not proteins




peptides proteins

TDMDNQIVVSDYAQMDRTW




IPI00302927IPI00025512IPI00002478IPI00185600IPI00014537IPI00298497IPI00329236IPI00002232

Protein Inference is complex!!



PROTEIN INFERENCE



Intermezzo: Protein inference

The minimal and maximal explanatory sets

peptide a b c d

proteinsprot X x xprot Y xprot Z x x x

Minimal setOccam {

peptide a b c d

proteinsprot X x xprot Y xprot Z x x x

Maximal setanti-Occam {

The Truth



Intermezzo: Protein inference

Slide from J. Cottrell, Matrix Science



Protein inference

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Protein inference

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Protein inference

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Protein inference

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Protein inference

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Protein inference

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Protein inference

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Protein inference

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Protein inference

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Unambiguous peptide



OTHER APPROACHES TO PERFORM MS/MS IDENTIFICATION





Spectral comparison



compare



Spectrallibrary



compare





Example of a manual de novo of an MS/MS spectrumNo more database necessary to extract a sequence!

Algorithms

LutefiskSherengaPEAKS

PepNovo…

References

Dancik 1999, Taylor 2000Fernandez-de-Cossio 2000

Ma 2003, Zhang 2004Frank 2005, Grossmann 2005

…

De novo approaches





Spectral comparison



compare



Spectrallibrary



compare





Spectral searching

• Concept: To compare experimental spectra to other experimental spectra.

• There are many spectral libraries publicly available (for instance, from NIST)

• Custom ‘search engines’ have been developed:• SpectraST (TPP)• X!Hunter (GPM)

• It has been claimed that the searches have more sensitivity that with sequence database approaches



Spectral searching (2)

http://peptide.nist.gov/






COMBINING DIFFERENT SEARCH APPROACHES



Multi-stage peptide identification strategy


Goal: “Squeeze” your good quality experimental spectra



PROTEIN SEQUENCE DATABASES



1. Comprehensive (whatever is not in the DB will not be included in your results).

2. Not too redundant at the protein sequence level- Protein inference gets easier- It is not very good if the database is too big.

3. Quality of annotation

4. Stability of identifiers

What is needed from a protein database



a) UniProt Knowledgebase (UniProtKB): SWISS-PROT (manually curated)/ TrEMBL.

b) NCBI non-redundant database: It compiles all protein sequences available from the following databases: ‘GenBank’ translations, the Protein Data Bank (PDB), UniProtKB/Swiss-Prot, PIR and PRF.

c) Ensembl: Genomics centric resource. Integration of the information with genomics is easy.

d) IPI (International Protein Index): It has been discontinued (9/2012). Different builds for different species (Human, Mouse, Cow, Rat, Zebrafish, Dog, Arabidopsis).

e) Model organisms DBs (for instance, TAIR for Arabidopsis).

Main databases used



- If the species is not well represented in the protein databases, there is a much stronger need to search ESTs or genomic databases.

- The search engine will translate the 6 possible ORFs for each nucleotide sequence.

- ESTs are not suitable for PMF approaches (incomplete proteins).

- The alternative is to filter comprehensive databases like UniProt by species or genus, or to use a protein DB from a close organism.

Databases for non-model organisms



- Since each database has a different focus, the databases can vary in terms of completeness, degree of redundancy, and quality of annotations.

- More inclusive bigger protein databases will take longer to search

- For the bigger resources, it may also result on more false-positive identifications and reduced statistical significance (the probability of random match is higher).

Importance of choosing the right DB



POST-VALIDATION OF RESULTS



- Concepts of peptide and protein FDR

- Decoy databases

- Softwares like PeptideProphet, ProteinProphet, …

- Influence of PTMs in the search

- Scoring of PTM positioning

…..

Other concepts that would be nice to learn…



Recommended reading….

Nesvizhskii, J Proteomics, 2010

and many more…



Conclusions

• Approaches to perform peptide and protein identification

• Sequence database based approaches: search engines

• The protein inference problem

• Importance of choosing the right protein database

• Many things to be learnt…



Remember: it should not be a black box…

From: Lilley et al., Proteomics, 2011



And still… we haven’t touched quantification at all

From: Vaudel et al., Proteomics, 2010



Questions?

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EBI is an Outstation of the European Molecular Biology Laboratory. MS Identification Dr. Juan...

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