PMP

EMBnet course

Proteomics using Bioinformatics tools

MS identification tools

Patricia M. PalagiPIG, SIB, Geneva

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The data: list of m/z values

840.6950 13.751676.9606 26.11498.8283 128.91045.564 845.22171.9670 2.56861.1073 371.2842.51458 53.71456.7274 12.9863.268365 3.1

MS

1163.7008 286.1105 220.142986.1738 13.7619102.0752 4.3810147.1329 57.3333185.1851 649.0953185.3589 5.3810186.1876 81.4286213.0791 1.4286

MS/MS

frag

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Peptide

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Parent mass chargeParent mass value

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The tools

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One direct access to all- ExPASy

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

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Automatic protein identification

- Peptide mass fingerprinting – PMF

- MS/MS sequence search

- MS/MS spectra library search

- MS/MS prospective analysis (tag, open mod, de novo sequencing

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Peptide mass fingerprinting = PMF MS database matching

Enzymaticdigestion

In-silicodigestion

Protein(s) Peptides840.6950861676.960631498.82831045.5642171.967066861.107346842.514581456.727405863.268365

Mass spectraPeaklist

…MAIILAGGHSVRFGPKAFAEVNGETFYSRVITLESTNMFNEIIISTNAQLATQFKYPNVVIDDENHNDKGPLAGIYTIMKQHPEEELFFVVSVDTPMITGKAVSTLYQFLV …

- MAIILAGGHSVR- FGPK- AFAEVNGETFYSR- VITLESTNMFNEIIISTNAQLATQFK- YPNVVIDDENHNDK…

Sequence database entry

861.107346838.6950861676.960631498.82831045.5642171.967066842.514581457.827405863.268453

Theoretical peaklist

Theoretical proteolytic peptides

Match

Result: ranked list of protein candidates

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Peptide mass fingerprinting

What you have:- Set of peptide mass values- Information about the protein: molecular weight, pI, species.- Information about the experimental conditions: mass spectrometer precision, calibration used, possibility of missed-cleavages, possible modifications- Biological characteristics: post-translational modifications, fragments

What will do the tool:- Match between this information and a protein sequence database

What will you get:- a list of probable identified proteins

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What is the expected information in a submission form?

• Place to upload a spectrum (many spectra)

• Description of the sample process used – Chemical process such as alkylation/reduction, – Cleavage properties (enzyme), – Mass tolerance (m/z tolerance)

• Search space – Sequence databank, – taxonomy restriction– Mw, pI restriction

• Scoring criteria and filters

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One example of parameter effects on the search

• Accepted mass tolerancedue to imprecise measures and calibration problems

Source: Introduction to proteomics: tools for the new biology. Daniel C. Liebler. Human Press. 2002

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Summary of PMF tools

Tool Source website

Aldente www.expasy.org/cgi-bin/aldente

Mascot www.matrixscience.com/

MS-Fit prospector.ucsf.edu/

ProFound prowl.rockefeller.edu/profound_bin/WebProFound.exe

PepMAPPER wolf.bms.umist.ac.uk/mapper/

PeptideSearch www.mann.embl-heidelberg.de/GroupPages/PageLink/peptidesearchpage.html

PepFrag prowl.rockefeller.edu/prowl/pepfragch.html

Non exhaustive list!

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Scoring systems

• Essential for the identification! Gives a confidence value to each matched protein

• Three types of scores

• Shared peaks count (SPC): simply counts the number of matched mass values (peaks)

• Probabilistic scores: confidence value depends on probabilistic models or statistic knowledge used during the match (obtained from the databases)

• Statistic-learning: knowledge extraction from the influence of different properties used to match the proteins (obtained from the databases)

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Mascot• Internet free version in the above website (commercial versions available too)•Choice of several databases.• Considers multiple chemical modifications.• 0 to 9 missed-cleavages. • Score based on a combination of probabilistic and statistic approaches (is based on Mowsescore).• Considers Swiss-Prot annotations for Splice Variants (in locally installed versions).

http://www.matrixscience.com/

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Mascot - principles• Probability-based scoring• Computes the probability P that a match is

random • Significance threshold p< 0.05 (accepting that

the probability of the observed event occurring by chance is less than 5%)

• The significance of that result depends on the size of the database being searched.

• Mascot shades in green the insignificant hits• Score: -10Log10(P)

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Mascot

Input

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Hints about the significance of the score

Decoy Output

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Sequence coverage

Peptides matched

Error function

Output

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Aldente • SwissProt/TrEMBL db, indexed masses (trypsine and many others).

• Considers chemical modifications and user specified modifications.

• Considers biological modifications (annotations SWISS-PROT).

• 0 or 1 missed-cleavages.

• Use of robust alignment method (Hough transform):

• Determines deviation function of spectrometer

• Resolves ambiguities

• Less sensitive to noise

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Aldente – summary

Exp

erim

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l m

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s /

pea

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Theoretical masses / peptides

Spectrometer calibration error

Spectrometer internal error

• The Hough Transform estimates from the experimental data the deviation function of the mass spectrometer (the calibration error function).

• The program optimizes the set of best matches, excluding noise and outliers, to find the best alignment.

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Aldente - Input

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Aldente - Input

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Aldente - Input

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Aldente - Input

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Aldente - Input

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Output

Hints about the significance of the score

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Information from Swiss-Prot annotation. Processed protein (signal peptide is cleaved).

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BioGraph

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• A summary of the search parameters

• A list of potentially identified proteins (AC numbers) with scores and other evidences

• A detailed list of potentially identified peptides (associated or not to the potentially identified proteins) with scores

• Possibilities to validate/invalidate the provided results (info on the data processing, on the statistics, links to external resources, etc.)

• Possibilities to export the (validated) data in various formats

What is the expected information in an identification result?

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- Exact primary structure- Splicing variants- Sequence conflicts- PTMs

1 protein entry does not represent1 unique molecule

Protein characterization with PMF data

Prediction tools• PTMs and AA substitutions • Oligosaccharide structures • Unspecific cleavages

FindModGlycoModFindPept

Characterization tools at ExPASy using peptide mass fingerprinting data http://www.expasy.org/tools/

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SWISS-PROT feature table: active protein is more than just translation of

gene sequence (example: P20366)

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Detection of PTMs in MS

624.

376

9.8

893.

4

994.

510

56.1

1326

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01.9

1759

.8 1923

.421

00.6

600 2200

624.

376

9.8

893.

4

994.

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56.1

1326

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01.9

1759

.819

23.4

2100

.6

600 2200

624.

376

9.8

893.

4

994.

510

56.1

1326

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4

994.

5

1070

.113

26.7

1501

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.4

2100

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Δ m/z => PTM Δ m/z => PTM

Unmodifiedtrypticmasses

Trypticmasses of a modifiedprotein

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FindModhttp://www.expasy.org/tools/findmod/

DB entry

experimental

masses

experimental

options

AA modifications

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FindMod Output

}

} unmodified peptides,modified peptidesknown in SWISS-PROT and chemically modified peptides

putatively modified peptides predictedby mass differences

+ putative AA substitutions

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Modification rules can be defined from SWISS-PROT, PROSITE and the literature

modification amino acid rule exceptions

farnesylation Cys -

palmitoylation Cys Ser, ThrO-GlcNAc Ser, Thr Asn

amidation Xaa (C-term) where Gly followed Xaapyrrolidone carboxylic acid Gln (N-term) -

phosphorylation in eukaryotes: Ser, Thr, Asp, His, Tyr -in prokaryotes: Ser, Thr, Asp, His, Cys -

sulfatation in eukaryotes Tyr, PROSITE PDOC00003

some examples:

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FindMod Output - Application of Rules

- potentially modified peptides that agree with rules are listed- amino acids that potentially carry modifications are shown

- peptides potentially modified only by mass difference

- predictions can be tested by MS-MS peptide fragmentation

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• From MS (peptide mass fingerprint) data -detection of :– Matching peptides for unspecific cleavage– Masses resulting from possible contaminants– Matching peptides for specific cleavage (16 different

enzymes)– Peptides resulting from protease autolysis

FindPepthttp://www.expasy.org/tools/findpept.html

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FindPep

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FindPep

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FindPep

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FindPep

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• Tag search- Tools that search peptides based on a MS/MS Sequence Tag

– MS-Tag and MS-Seq, PeptideSearch

• Ion search or PFF - Tools that match MS/MS experimental spectra with “theoretical spectra” obtained via in-silicofragmentation of peptides generated from a sequence database– Phenyx, Mascot, Sequest, X!Tandem, OMSSA, ProID, …

• de novo sequencing - Tools that directly interpret MS/MS spectra and try to deduce a sequence– Convolution/alignment (PEDENTA)– De-novo sequencing followed by sequence matching

(Peaks, Lutefisk, Sherenga, PeptideSearch)– Guided Sequencing (Popitam)

In all cases, the output is a peptide structure per MS/MS spectrum

MS/MS based identification tools

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Peptide fragmentation fingerprinting = PFF = ion search

MS/MS database matchingEnzymaticdigestion

In-silicodigestion

Protein(s) Peptides340.695086676.96063498.8283545.5641171.967066261.107346342.51458456.727405363.268365

MS/MS spectra of peptides

Ions peaklists

…MAIILAGGHSVRFGPKAFAEVNGETFYSRVITLESTNMFNEIIISTNAQLATQFKYPNVVIDDENHNDKGPLAGIYTIMKQHPEEELFFVVSVDTPMITGKAVSTLYQFLV …

- MAIILAGGHSVR- FGPK- AFAEVNGETFYSR- VITLESTNMFNEIIIK- YPNVVIDDENNDK…

Sequence database entry

361.107346338.695086676.96063498.82831045.5641171.967066342.51458457.827405263.268453

Theoretical peaklist

Theoretical proteolytic peptides

Match

Result: ranked list of peptide

and protein

candidates

Theoretical fragmented

peptides

-MAIILAG-MAIILA-MAIIL-MAII-MAI-M-M-AIILAG

In-silicofragmentation

PMP

P' nterm P' cterm

offset-28-45-46

0-17-18

+17

[N] is the mass of the N-term group[M] is the mass of the sum of the neutral amino acid residue masses

It is very important to know the ionic series produced by a spectrometer, otherwise potential matches will be missed.

In the other hand, if an ion-type not present in the original spectrum is taken into account, it will contribute to get false positive matches.

Ion-types

+28+ 2-15-16

-15

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Some PFF tools

Software Source website

InsPecT peptide.ucsd.edu/inspect.py

Mascot www.matrixscience.com/search_form_select.html

MS-Tag and MS-Seq prospector.ucsf.edu

PepFrag prowl.rockefeller.edu/prowl/pepfragch.html

Phenyx phenyx.vital-it.ch

Popitam www.expasy.org/tools/popitam

ProID (download) sashimi.sourceforge.net/software_mi.html

Sequest* fields.scripps.edu/sequest/index.html

Sonar 65.219.84.5/service/prowl/sonar.html

SpectrumMill* www.home.agilent.com

VEMS www.bio.aau.dk/en/biotechnology/vems.htm

X!Tandem (download) www.thegpm.org/TANDEM

*Commercialized

Same principle of a PMF, but using MS/MS spectra

Non exhaustive list!

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Phenyx

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The Phenyx Web Interface:One result, multiple views

Excel, xml and text exports

DesktopResultsviews

Submission

Management consoleResults comparison

P.A. Binz

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The Proteins overview

List of identifiedproteins

Corresponding list of identified peptides

Protein group description

P.A. Binz

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The Proteins overview

Hints about the significance of the score

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Hints about the significance of the score

Better when high intensity peaks are matched and ion series are extended, without too many and too big holes

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The scoring system in Phenyx

• The score is the sum of up to 12 basic scores such as:– presence of a, b, y, y++, B-H2O…; co-occurrence of ion series

(using HMMs), peak intensities, residue modifications (PTM or chemical), …

• True probabilistic approach for each peptide match

(likelihood of being correct)log --------------------------------

(likelihood of being random)

• Function of instruments and molecular types– Esquire 3000+, LCQ; iTRAQ vs. unmodified peptides

• Scores are normalised into z-scores

Search in a query database

Search in a randomized set of peptides

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X!Tandem

www.thegpm.org

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X!Tandem - output

1

2

3

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The two-rounds searchMascot, Phenyx and X!Tandem

The identification process may be launched in 2-rounds

• Each round is defined with a set of search criteria– First round searches the selected database(s) with

stringent parameters,– Second round searches the proteins that have

passed the first round (relaxed parameters):

⇒Accelerate the job when looking for many variable modifications, or unspecific cleavages

⇒Appropriate when the first round defines stringent criteria to capture a protein ID, and the second round looks for looser peptide identifications

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Example 2nd round

1rnd,Only 3 fixed mods131 valid, 75% cov.

2rnd,Add variable mods205 valid,84% cov.

2rnd,With all modsAnd half cleaved348 valid,90% cov.

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Source of errors in assigning peptides

• Scores not adapted

• Parameters are too stringent or too loose

• Low MS/MS spectrum quality (many noise peaks, low signal to noise ratio, missing fragment ions, contaminants)

• Homologous proteins

• Incorrectly assigned charge state

• Pre-selection of the 2nd isotope (the parent mass is shifted of 1 Da. A solution is to take the parent mass tol. larger, but may drawn the good peptide too)…

• Novel peptide or variant

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Hints to know when the identification is correct

• Good sequence coverage: the larger the sub-sequences and the higher the sequence coverage value, the better

• Consider the length of the protein versus the number of matched theoretical peptides

•Better when high intensity peaks have been used in the identification

•Scores: the higher, the better. The furthest from the 2nd hit the better

• Filter on the correct species if you know it (reduces the search space, time, and errors)

• Better when the errors are more or less constants among all peptides found.

•If you have time, try many tools and compare the results

With MS

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Hints to know when the identification is correct

• The higher the number of peptides identified per protein, the better

• Sequence coverage: the larger the sub-sequences and the higher the sequence coverage value, the better

•Depends on the sample complexity and experiment workflow

• Scores: the higher, the better.

• Filter on the correct species if you know it (reduces the search space, time, and errors)

• Better when high intensity peaks are matched and ion series areextended, without too many and too big holes.

• Better when the errors are more or less constants among all ions.

• If you have time, try many tools and compare the results

With MS/MS

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E-values

• For a given score S, it indicates the number of matches that are expected to occur by chance in a database with a score at least equal to S.

• The e-value takes into account the size of the database that was searched. As a consequence it has a maximum of the number of sequences in the database.

• The lower the e-value, the more significant the score is.

• An e-value depends on the calculation of the p-value.

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p-value

• A p-value describes the probability, which assesses the chance of validly rejecting the null hypothesis. If the p-value is 10-5 then the rejection of the null hypothesis is due to chance with a probability of 10-5.

• A p-value has a maximum of 1.0.

• The larger the search space, the higher the p-value since the chance of a peptide being a random match increases.

• The lower the p-Value, the more significant is the match.

Source: Lisacek, Practical Proteomics, 2006 Sep;6 Suppl 2:22-32

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Z-score• Z-score is a dimensionless quantity

derived by subtracting the population mean from an individual (raw) score and then dividing the difference by the population standard deviation.

• The z score reveals how many units of the standard deviation a case is above or below the mean.

σμ−

=−x

scoreZ

Source: wikipedia

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So what?

• For small (significant) p-values, p and e are approximately equal, so the choice of one or the other is often equivalent. It is therefore reasonable to assimilate low p-values in Phenyx to e-values. X!Tandem simply switches e-values to log values to remove the powers of 10

• For a single search (or set of sampled peptides), you can compare z-scores. However, when two or more searches are performed on different size spaces, you first need to look at the p-values before comparing z-scores.

Source: Lisacek, Practical Proteomics, 2006 Sep;6 Suppl 2:22-32