Given an input file of molecular weights corresponding to peptides cut by proteolytic enzymes or reagents, emowse will search the supplied input protein sequences for digest fragments that match the molecular weights. For each input sequence, emowse derives both whole sequence molecular weight and calculated peptide molecular weights for complete digests. One of eight cutting enzymes/reagents can be specified and an optional whole sequence molecular weight (if known). Optionally, monoisotopic weights are used. emowse also incorporate calculated peptide Mw's resulting from incomplete or partial cleavages. At present, this is achieved by computing all nearest-neighbour pairs for each enzyme or reagent.
emowse writes an output file that includes: i. The specified search parameters (digest reagent, specified error tolerance, specified intact protein Mw and Mw filter percentage). ii. Short 'hit' listing (the top 50 scoring proteins listed in descending order, the sequence ID name and brief text identifiers are included). iii. Detailed 'hit' listing (the top 50 entries listed in more detail).
|
|
The input file is a list of molecular weights of the peptide fragments. One weight is allowed per line. The example file above is a Trypsin digest of the protein sw:100K_rat (produced by using the program digest).
Each molecular weight must be on a line of its own. Masses (M not M[H+]) are accepted in any order (ascending,descending or mixed). Peptide masses can be entered as integers or floating-point values, the latter being rounded to the nearest integer value for the search.
It is suggested that peptide masses should be selected from the range 700-4000 Daltons. This range balances the fact that very small peptides give little discrimination and minimizes the frequency of partially-cleaved peptides.
As a general rule, users are advised to identify and remove peptide masses resulting from autodigestion of the cleavage enzyme (e.g tryptic fragments of trypsin), best obtained by MS analysis of control digests containing only the enzyme.
Further information on the partial sequence and/or composition of the peptides can be given after the weight with a 'seq()' or 'comp()' specification. This should be formatted like:
mw seq(...) comp(...)
where mw is the molecular mass of the fragment, seq(...) is sequence information and comp(...) is composition information. A line may contain more than one sequence information qualifiers. For example:
7176 seq(b-t[pqt]ln) 1744 1490 1433 comp(3[ed]1[p]) seq(gmde)
Sequence information
The sequence information should be given in standard One-character code. It should be preceded by a prefix as outlined in the table below, to indicate what type of sequence it is.
| Prefix | Meaning | Example |
|---|---|---|
| b- | N->C sequence | seq(b-DEFG) |
| y- | C->N sequence | seq(y-GFED) |
| *- | Orientation unknown | seq(*-DEFG) seq(*-GFED) |
| n- | N terminal sequence | seq(n-ACDE) |
| c- | C terminal sequence | seq(c-FGHI) |
| The examples are all correct data for a
peptide with a sequence ACDEFGHI. Note that *-DEFG will search for both DEFG and GFED | ||
Both lower and upper case characters may be used for amino-acids. An unknown amino acid may be indicated by an 'X'. More than one amino acid may be specified for a position by putting them between square brackets. A line may contain several sequence information qualifiers. An example for a peptide with the actual sequence ACDEFGHI might look like:
12345 seq(n-AC[DE]) seq(c-HI)
comp(2[H]0[M]3[DE]*[K])indicates a peptide which contains 2 histidines, no methionines, 3 acidic residues (glutamic or aspartic acid) and at least 1 lysine.
The emowse search program outputs a listing file containing the following information.
Matching peptides marked with a '*' denote partially-cleaved fragments.
Given a one-per-line file of molecular weights cut by enzymes/reagents, emowse will search a protein database for matches with the mass spectrometry data.
One of eight cutting enzymes/reagents can be specified and an optional whole sequence molecular weight.
Determination of molecular weight has always been an important aspect of the characterization of biological molecules. Protein molecular weight data, historically obtained by SDS gel electrophoresis or gel permeation chromatography, has been used establish purity, detect post-translational modification (such as phosphorylation or glycosylation) and aid identification. Until just over a decade ago, mass spectrometric techniques were typically limited to relatively small biomolecules, as proteins and nucleic acids were too large and fragile to withstand the harsh physical processes required to induce ionization. This began to change with the development of 'soft' ionization methods such as fast atom bombardment (FAB)[1], electrospray ionisation (ESI) [2,3] and matrix-assisted laser desorption ionisation (MALDI)[4], which can effect the efficient transition of large macromolecules from solution or solid crystalline state into intact, naked molecular ions in the gas phase. As an added bonus to the protein chemist, sample handling requirements are minimal and the amounts required for MS analysis are in the same range, or less, than existing analytical methods.
As well as providing accurate mass information for intact proteins, such techniques have been routinely used to produce accurate peptide molecular weight 'fingerprint' maps following digestion of known proteins with specific proteases. Such maps have been used to confirm protein sequences (allowing the detection of errors of translation, mutation or insertion), characterise post-translational modifications or processing events and assign disulphide bonds [5,6].
Less well appreciated, however, is the extent to which such peptide mass information can provide a 'fingerprint' signature sufficiently discriminating to allow for the unique and rapid identification of unknown sample proteins, independent of other analytical methods such as protein sequence analysis.
Practical experience has shown that sample proteins can be uniquely identified using as few as 3- 4 experimentally determined peptide masses when screened against a fragment database derived from over 50,000 proteins. Experimental errors of a few Daltons are tolerated by the scoring algorithms, permitting the use of inexpensive time-of-flight mass spectrometers. As with other types of physical data, such as amino acid composition or linear sequence, peptide masses can clearly provide a set of determinants sufficiently unique to identify or match unknown sample proteins. Peptide mass fingerprints can prove as discriminating as linear peptide sequence, but can be obtained in a fraction of the time using less material. In many cases, this allows for a rapid identification of a sample protein before committing to protein sequence analysis. Fragment masses also provide structural information, at the protein level, fully complementary to large-scale DNA sequencing or mapping projects [7,8,9].
For each entry in the specified set of sequences to search, emowse derives both whole sequence molecular weight and calculated peptide molecular weights for complete digests using the range of cleavage reagents and rules detailed in Table 1. Cleavage is disallowed if the target residue is followed by proline (except for CNBr or Asp N). Glu C (S. aureus V8 protease) cleavages are also inhibited if the adjacent residue is glutamic acid. Peptide mass calculations are based entirely on the linear sequence and use the average isotopic masses of amide-bonded amino acid residues (IUPAC 1987 relative atomic masses). To allow for N-terminal hydrogen and C-terminal hydroxyl the final calculated molecular weight of a peptide of N residues is given by the equation:
N
__
\
/ Residue mass + 18.0153
--
n=1
Molecular weights are rounded to the nearest integer value before being
used. Cysteine residues are calculated as the free thiol, anticipating
that samples are reduced prior to mass analysis. CNBr fragments are
calculated as the homoserine lactone form. Information relating to
post- translational modification (phosphorylation, glycosylation etc.)
is not incorporated into calculation of peptide masses.
Reagent no. Reagent Cleavage rule
1 Trypsin C-term to K/R
2 Lys-C C-term to K
3 Arg-C C-term to R
4 Asp-N N-term to D
5 V8-bicarb C-term to E
6 V8-phosph C-term to E/D
7 Chymotrypsin C-term to F/W/Y/L/M
8 CNBr C-term to M
Current versions of emowse also incorporate calculated peptide Mw's resulting from incomplete or partial cleavages. At present, this is achieved by computing all nearest-neighbour pairs for each enzyme or reagent detailed in table 1.
Computing all possible nearest-neighbour partial fragments adds significantly to the number of peptides entered in the database (by a factor of two). The major effect of this is to increase the background score by increasing the number of random Mw matches, which can significantly reduce discrimination. The use of a low '-partials' factor (eg 0.1 - 0.3) is a useful way of limiting this effect - partial peptide matches will add a little to the cumulative frequency score, but without compromising discrimination.
More experienced users can utilise the '-partials' factor to optimize searches where the peptide Mw data contain a significant proportion of partial cleavage fragments (eg > 30%). In such cases, setting the '-partials' factor within the range 0.4 - 0.6 can help to improve discrimination. Conversely, if the digestion is perfect, with no partial fragments present, the lowest '-partials' factor of 0.1 will give maximum discrimination.
For each peptide Mw entry in the data file, emowse matches individual fragment molecular weights (FMWs) with database entry molecular weights (DBMWs). A 'hit' is scored when the following criterion is met:
DBMW-tolerance-1 < FMW < DBMW+tolerance+1
If an intact protein Mw is specified (SMW) then the program prompts for a molecular weight filter percentage (MWFP). emowse then restricts the search to those entries which match the following criteria:
R = SMW x MWFP / 100
0 < SMW-R < emowse entry Mol.wt. < SMW+R
Default search parameters are a tolerance of +/- 2 Daltons, intact Mw specified and the MWFP set to 25.
Cell value = Old value / Fmax
to yield floating point numbers between 0 and 1. These distribution frequency values, calculated for each cleavage reagent, were then built into the emowse search program. For every database entry scanned, all matching fragments contribute to the final score. In the current implementation, non-matching fragments are ignored (neutral). For each matching peptide Mw a score is assigned by looking up the appropriate normalised distribution frequency value. In the case of multiple 'hits' in any one target protein (i.e. more than one matching peptide Mw), the distribution frequency scores are multiplied. The final product score is inverted and then normalised to an 'average' protein Mw of 50 kDaltons to reduce the influence of random score accumulation in large proteins (>200 kDaltons). The final score is thus calculated as:
Score = 50/(Pn x H)
Where Pn is the product of n distribution scores and H the 'hit' protein molecular weight in kD.
Important consequences of this type of scoring scheme are that matches with peptides of higher Mw carry more scoring weight, and that the non-random distribution of fragment molecular weights in proteins of different sizes is compensated for.
This distribution was somewhat dependent on protein size, as smaller proteins generally yield fewer peptide fragments. Thus, all proteins of 30 kD and over were identified using 13% or less of possible fragments (1 in 8), with all proteins of 40 kD and above requiring less than 10% (1 in 10). In this latter group, therefore, more than 90% of the potential information (all possible peptides) was redundant. For the simulation a 'unique' identification required matching not only of protein type (e.g. globin) but correct discrimination of type, species, and isoform or isozyme. Discrimination could be further improved by reducing the error tolerance to only +/- 1 Dalton (mean=2.7 peptides). Such accuracies are easily bettered using more sophisticated ESI/quadrupole or high-field FAB spectrometers, but the default search value (+/- 2 Daltons) compensates for the reduced accuracy obtainable from the smaller time-of-flight (TOF) instruments. Mass accuracies better than +/- 1 Dalton were not essential, and in fact the error tolerance could be relaxed to +/- 5 Daltons in many cases with little degradation in performance. The simulation thus clearly demonstrated the high degree of discrimination afforded by relatively few peptide masses, even with generous allowance for error.
Other references: