1. Peptide identification by peptide fragmentation fingerprinting
Aims: Introduce concepts and tools for PFF

2. Peptide fragmentation fingerprinting
PFF = ion search MS/MS database matching
Protein(s)
Peptides
Ions peaklists
MS/MS spectra of peptides
Enzymatic
digestion
Result: ranked list of peptide and protein candidates
Match
In-silico
fragmentation
MAIILAG
MAIILA
MAIIL
MAII
MAI M
AIILAG
…MAIILAGGHSVRFGPKAFAEVNGETFYSRVITLESTNMFNEIIISTNAQLATQFKYPNVVIDDENHNDKGPLAGIYTIMKQHPEEELFFVVSVDTPMITGKAVSTLYQFLV …
In-silico
digestion
- MAIILAGGHSVR
- FGPK
- AFAEVNGETFYSR
- VITLESTNMFNEIIIK
- YPNVVIDDENNDK …
Theoretical
fragmented peptides
Sequence
database entry
Theoretical
proteolytic peptides
Theoretical
peaklist

3. Peaklist file formats
Heterogeneity: each constructor, even each instrument has its own file format
Different information and type of data across formats
Raw data often not available
Raw data format often not known
Libraries for reading formats often not available

4. Main file formats Example DTA (data) …
DTA (data)
Company: Thermo Electron + Sequest
Precursor mass unit: [M+H]+
Precursor Intensity: yes
Format:
Single or Multiple spectra

5. Main file formats MGF (Mascot generic format) Company: Matrix Science
BEGIN IONS
TITLE=A
CHARGE=2+
PEPMASS= …
END IONS
MGF (Mascot generic format)
Company: Matrix Science
Precursor mass unit : m/z
Precursor Intensity: optional
Format:
Single or Multiple spectra

6. Precursor value in DTA and Mascot
In a DTA file, the precursor peptide mass is an MH+ value independent of the charge state.
In Mascot generic format, the precursor peptide mass is an observed m/z value, from which Mr or MHnn+ is calculated using the prevailing charge state.
For example, in Mascot:
PEPMASS=1000 CHARGE=2+
... means that the relative molecular mass Mr is This is equivalent to a DTA file which starts:
1999 2
Source: Matrix Science site

7. MS/MS based identification tools
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-silico fragmentation 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

8. Some PFF tools Same principle of a PMF, but using MS/MS spectra
Software
Source website
InsPecT
peptide.ucsd.edu/inspect.py
Mascot
MS-Tag and MS-Seq
prospector.ucsf.edu
PepFrag
prowl.rockefeller.edu/prowl/pepfragch.html
Phenyx
phenyx.vital-it.ch
Popitam
ProID (download)
sashimi.sourceforge.net/software_mi.html
Sequest*
fields.scripps.edu/sequest/index.html
Sonar
/service/prowl/sonar.html
SpectrumMill*
VEMS
X!Tandem (download)
Non exhaustive list!
*Commercialized

9. MASCOT

10. Sequest / Turbo Sequest
Algorithm (peptide matching)
Experimental spectrum: data reduction
Convert m/z ratios to their nominal values (nearest integer)
Remove all but the 200 most abundant ions
Divide spectrum into 10 evenly spaced sub-sections
In each sub-section: normalize ions to the most abundant ion in the sub-section, which is given value of 50
Nominal values: important: we have discret space, unit: 1 m/z

11. Data reduction
Experimental spectrum
Processed spectrum

12. Sequest – Algorithm (II)
Build theoretical spectra
For each peptide sequence:
Calculate b- and y-ions from sequence; assign intensity value of 50
Calculate neutral loss ions for ammonia, water losses and a-ions; assign values of 10
Add width to b- and y-ions: assign value of 25 to m/z ± 1 from calculated b- and y-ions
Same for neutral loss and a-ions; assign value of 10

13. Build theoretical spectra – examples

14. Sequest – Algorithm (III)
Candidates selection
For each theoretical spectrum:
Compute simple correlation with processed experimental spectrum
(Sp Shared Peak Counts [with intensities])
Keep 500 top ranked spectra (peptides)

15. Sequest – Algorithm (IV)
Compute cross-correlation (XCorr)
For each of 500 theoretical spectra:
Compute XCorr correlation
Sort peptides by XCorr result
Top one is best peptide match
deltCni = (XCorrmax-XCorri) / XCorrmax

16. Sp score where Ik = intensity of matched peak k,
m = number of matches,
b = reward for consecutive match of an ion series,
r = reward for presence of immonium ion
L = number of theoretical ions
SP: correlation of matching peaks only
Reward

17. Xcorr score where E = experimental spectrum T = theoretical spectrum
xi and yi are intensity values of E and T
t = displacement value
N = number of data points in spectrum
Cross-correlation is computed using a Fast Fourier Transform (FFT)
Xcorr: correlation of the whole MS

18. Sequest/TurboSequest
Transformation of experimental masses: intensities are normalised, low-intensity peaks are removed, m/z values are round to next integer.
Virtual digestion of the database which considers also combinations of modifications.
Match between the theoretical and experimental masses and computation of the score:
Reconstruction of a virtual spectrum of the 500 best peptide sequences with three different intensity values.
Selection of 500 best peptides.
Cross-correlation: Fourier transform of both signals (experimental and virtual) and multiplication of results.
Summary of the last 9 slides

19. Sequest output
Problem: How to know if a hit is correct

20. X!Tandem

21. X!Tandem
Calculates statistical confidence (e-values) for all of the individual spectrum-to-sequence assignments
Reassembles all of the peptide assignments in a data set onto the known protein sequences and assign the statistical confidence that this assembly and alignment is non-random.
E- values are then transformed to log values to remove the powers of 10

22. X!Tandem Match experimental versus theoretical spectra
Preliminary score = dot product of experimental versus theoretical spectra (because only similar peaks are considered, this is the sum of the intensities of the matched y and b ions)
Hyperscore = the preliminary score by multiplying by N factorial for the number of b and y ions assigned
It makes a histogram of all the hyperscores for all the peptides in the database that might match this particular spectrum
Log transformation of these values, a line interpolates them
A match is significant if is greater than the point at which the straight line through the log data intersects the log(#results)=0 line.
Source: Brain Searle (Proteome Software) – XTandem to be explained (to be asked permission)

23. Log transformation and e-value
X!Tandem calculates the E-value by extrapolating the red line of the log histogram.
For the example shown, a hyperscore of 83 would occur by chance where the red line crosses 83. The log of this value — the E-value — is -8.2, as shown.
hyperscore
log(# results)
E-value=e-8.2
Source: Brain Searle (Proteome Software) – XTandem to be explained (to be asked permission)

24. X!Tandem - output 1 2 3

25. The two-rounds search: Mascot 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
The first round is typically run with stringent parameters on an extended database to achieve a rough isolation of proteins of the sample while keeping combinatorial explosion under control. The second round is performed with looser parameters on a limited protein database built from the potentially identified proteins. Unspecific cleavages, mutations or a wider range of posttranslational modifications can be explored during the second round.

26. Example 2nd round 1rnd, Only 3 fixed mods 131 valid, 75% cov. 2rnd,
Add variable mods
205 valid,
84% cov.
2rnd,
With all mods
And half cleaved
348 valid,
90% cov.

27. De novo sequencing
Sequencing = « read » the full peptide sequence out of the spectrum (from scratch)
Then, eventually search database for sequence (not necessarily)

28. Why de novo sequencing is difficult
Leucine and isoleucine have the same mass
Glutamine and lysine differ in mass by 0.036Da
Phenylalanine and oxidized methionine differ in mass by 0.033Da
Cleavages do not occur at every peptide bond (or cannot be observed on the MS-MS
Poor quality spectrum (some fragment ions are below noise level)
The C-terminal side of proline is often resistant to cleavage
Absence of mobile protons
Peptides with free N-termini often lack fragmentation between the first and second amino acids

29. Why de novo sequencing is difficult (II)
Certain amino acids have the same mass as pairs of other amino acids
Gly +Gly ( ) Asn ( )
Ala +Gly ( ) Gln ( )
Ala +Gly ( ) Lys ( )
Gly + Val ( ) Arg ( )
Ala + Asp ( ) Trp ( )
Ser + Val ( ) Trp ( )
Directionality of an ion series is not always known (are they b- or y-ions?)
Gly – G - Glycine 
Asp – D – Aspartic Acid
Asn – N - Asparagine
Gln – Q - Glutamine
Trp – W - Tryptophan

30. de novo sequencing methods
Manual inference by an expert
Creation of all combinations of possible sequences and comparison with the spectrum  exponential with the length of the peptide
Same thing but with prefixed filtering
Genetic algorithm approach
Spectrum transformed into a transition acyclic graph then search of the best path through the graph: SeqMS, SHERENGA, LUTEFISK97

31. Summary of de novo sequencing tools
Software
Source website
PEAKS
SeqMS (download)
Sherenga (included in SpectrumMill)
N/A
Lutefisk (download)  
DeNovoX*
PepNovo
peptide.ucsd.edu/pepnovo.py
SpectrumMill*
*Commercialized

32. 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

33. 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
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 are extended, without too many and too big holes.
Better when the errors are more or less constant among all ions.
If you have time, try many tools and compare the results

34. 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.

35. Decoy database
Is used to repeat the search, using identical search parameters, against a database in which the sequences have been reversed or randomised.
Do not expect to get any real matches from the "decoy" database
Helps estimate the number of false positives that are present in the results from the real database.
It is a good validation method for MS/MS searches of large data sets, it is not as useful for a search of a small number of spectra, because the number of matches is too small to give an accurate estimate.
In Mascot, a random sequence of the same length is automatically generated with the same average amino acid composition

36. Possible Decoy databases
Original sequence
Reverse: each sequence of the real database is reversed (back to forth)
Shuffle: each sequence of the real database is shuffled with the same average AA composition
Random: each sequence of the real database is a new randomised sequence based on the AA composition of the database