|
emowse |
Function
Protein identification by mass spectrometryDescription
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.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.
Table 1: Cleavage reagents modelled by emowse.
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.
Tolerance
The supplied number specifies the error allowed for mass accuracy of experimental mass determination. If no figure is specified, a default tolerance of 2 Daltons will be assumed. If you wish to specify a different tolerance then follow the qualifier '-tolerance' with the required number of Daltons. eg: '-tolerance 1'. In this case, supplied peptide masses will be matched to +/- 1 Daltons. Values of 2-4 are suggested for data obtained by laser- desorption TOF instruments. Accuracies of +/- 2 Daltons or better are generally only possible using an appropriate internal standard (e.g. oxidised insulin B chain) with TOF instruments. For electrospray or FAB data, a value of 1 can be selected in most cases. If you have real confidence in mass determination, specify '0' (zero) to limit matches to the nearest integer value (effectively +/- 0.5 Daltons). Discrimination is significantly improved by the selection of a small error tolerance.Whole sequence molecular weight
This option allows you to give the molwt of the whole protein (if known). This allows you to limit the search to proteins of this molwt plus/minus a 'limit' (see below). If unspecified, a whole protein molwt of 0 is assumed which emowse interprets as "search the whole database". This will include all proteins up to the maximum size of just under 700,000 Daltons. You can specify any molwt in Daltons with this command e.g. '-weight 90000'.Allowed whole sequence weight variability
This option is used in conjunction with the '-weight' option and is meaningless without it. It specifies a percentage. Only proteins of the given Sequence molecular weight +/- this percentage will be searched. If a Sequence molecular weight is specified but '-pcrange' is unspecified then '-pcrange ' will default to 25%. To specify a percentage of 30% use: '-pcrange 30'. In this case, a molecular weight of 90,000 Daltons was specified and the selection of 30 for the filter restricts the search to those proteins with masses from 63,000 to 117,000 Daltons. A value of 25 is suggested for initial searches, which can be progressively widened for subsequent search attempts if no matches are found. Discrimination is best when the filter percentage is narrow, but some Mw estimates (particularly from SDS gels) should be given considerable allowance for error.Partials factor
This specifies the weighting given to partially-cleaved peptide fragments, with a range from 0.1 to 1.0. If not specified, the default value is 0.4. The factor effectively down-weights the score awarded to a partial fragment by the specified amount. For example, a '-partials' of 0.25 will reduce the score of partial fragments to 25% (one quarter) of the score of a complete ('perfect') peptide cleavage fragment of equal mass.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.
Program requirements
The emowse search program accepts a single text file containing a list of experimentally-determined masses, generally selected from the range 700-4,000 Daltons to reduce the influence of partial cleavage products. The program outputs a ranked hit list comprising the top 30 scores, with information including the protein entry name, text identifiers, final accumulated scores, matching peptide sequences and hit versus miss tallies. User-selectable search parameters include an error tolerance (default +/- 2 Daltons), selection of the enzyme or reagent used and an intact protein Mw (optional, if known).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.
emowse Scoring scheme
The final scoring scheme is based on the frequency of a fragment molecular weight being found in a protein of a given range of molecular weight. OWL database sequence entries were initially grouped into 10 kDalton intact molecular weight intervals. For each 10 kDalton protein interval, peptide fragment molecular weights were assigned to cells of 100 Dalton intervals. The cells therefore contained the number of times a particular fragment molecular weight occurred in a protein of any given size. This operation was performed for each enzyme. Cell frequency values were calculated by dividing each cell value by the total number of peptides in each 10 kD protein interval. Cell frequency values for each 10 kDalton interval were then normalised to the largest cell value (Fmax), with all the cell values recalculated as:
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.
Simulation studies
In a simulation of scoring properties, 100 test proteins with masses between 10 kD and 100 kD were randomly selected from the OWL sequence database. The sets of all possible tryptic peptide masses for each protein were randomized and database searches performed with increasing numbers of fragments (default search parameters) until the test protein reached the top of the ranked scoring list. 99% of the test proteins were correctly identified using only five peptides or less (mean=3.6 peptides), with one example requiring six. These figures were surprisingly small considering that some of the proteins in the test sample generated more than 100 possible tryptic fragments. All 100 test examples were identified using 30% or less of the maximum number of available peptides.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.
Usage
Here is a sample session with emowse
% emowse Protein identification by mass spectrometry Input sequence(s): tsw:* Name of molecular weight data file: test.mowse Whole sequence molwt [0]: Output file [100k_rat.emowse]: |
Go to the input files for this example
Go to the output files for this example
Command line arguments
Standard (Mandatory) qualifiers:
[-sequence] seqall Sequence database USA
[-infile] infile Name of molecular weight data file
-weight integer Whole sequence molwt
[-outfile] outfile Output file name
Additional (Optional) qualifiers: (none)
Advanced (Unprompted) qualifiers:
-aadata datafile Molecular weight data for amino acids
-frequencies datafile Frequencies file
-enzyme menu Enzyme or reagent
-pcrange integer Allowed whole sequence weight variability
-tolerance float Tolerance
-partials float Partials factor
Associated qualifiers:
"-sequence" associated qualifiers
-sbegin1 integer Start of each sequence to be used
-send1 integer End of each sequence to be used
-sreverse1 boolean Reverse (if DNA)
-sask1 boolean Ask for begin/end/reverse
-snucleotide1 boolean Sequence is nucleotide
-sprotein1 boolean Sequence is protein
-slower1 boolean Make lower case
-supper1 boolean Make upper case
-sformat1 string Input sequence format
-sdbname1 string Database name
-sid1 string Entryname
-ufo1 string UFO features
-fformat1 string Features format
-fopenfile1 string Features file name
"-outfile" associated qualifiers
-odirectory3 string Output directory
General qualifiers:
-auto boolean Turn off prompts
-stdout boolean Write standard output
-filter boolean Read standard input, write standard output
-options boolean Prompt for standard and additional values
-debug boolean Write debug output to program.dbg
-verbose boolean Report some/full command line options
-help boolean Report command line options. More
information on associated and general
qualifiers can be found with -help -verbose
-warning boolean Report warnings
-error boolean Report errors
-fatal boolean Report fatal errors
-die boolean Report deaths
|
| Standard (Mandatory) qualifiers | Allowed values | Default | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [-sequence] (Parameter 1) |
Sequence database USA | Readable sequence(s) | Required | ||||||||||||||||
| [-infile] (Parameter 2) |
Name of molecular weight data file | Input file | Required | ||||||||||||||||
| -weight | Whole sequence molwt | Any integer value | 0 | ||||||||||||||||
| [-outfile] (Parameter 3) |
Output file name | Output file | <sequence>.emowse | ||||||||||||||||
| Additional (Optional) qualifiers | Allowed values | Default | |||||||||||||||||
| (none) | |||||||||||||||||||
| Advanced (Unprompted) qualifiers | Allowed values | Default | |||||||||||||||||
| -aadata | Molecular weight data for amino acids | Data file | Eamino.dat | ||||||||||||||||
| -frequencies | Frequencies file | Data file | Efreqs.dat | ||||||||||||||||
| -enzyme | Enzyme or reagent |
|
1 | ||||||||||||||||
| -pcrange | Allowed whole sequence weight variability | Integer from 0 to 75 | 25 | ||||||||||||||||
| -tolerance | Tolerance | Number from 0.100 to 1.000 | 0.1 | ||||||||||||||||
| -partials | Partials factor | Number from 0.100 to 1.000 | 0.4 | ||||||||||||||||
Input file format
Input files for usage example
'tsw:*' is a sequence entry in the example protein database 'tsw'
File: test.mowse
6082.8 5423.0 3086.3 2930.3 2424.7 2030.2 1399.6 1086.2 |
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)
Composition Information
Composition should consist of a number, followed by the corresponding amino acid between square brackets. For examplecomp(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.
Output file format
Output files for usage example
File: 100k_rat.emowse
Using data fragments of:
1086.2
1399.6
2030.2
2424.7
2930.3
3086.3
5423.0
6082.8
1 100K_RAT 100 KDA PROTEIN (EC 6.3.2.-).
2 SYV_FUGRU VALYL-TRNA SYNTHETASE (EC 6.1.1.9) (VALINE--TRNA LIGASE) (VALR
3 TCPD_FUGRU T-COMPLEX PROTEIN 1, DELTA SUBUNIT (TCP-1-DELTA) (CCT-DELTA).
4 FLAV_ECOLI FLAVODOXIN 1.
5 OPS2_DROME OPSIN RH2 (OCELLAR OPSIN).
6 FLAV_KLEPN FLAVODOXIN.
7 FLAV_SYNY3 FLAVODOXIN.
8 E2BB_FUGRU TRANSLATION INITIATION FACTOR EIF-2B BETA SUBUNIT (EIF-2B GDP-
9 FLAV_HAEIN FLAVODOXIN.
10 HIRA_FUGRU HIRA PROTEIN (TUP1 LIKE ENHANCER OF SPLIT PROTEIN 1).
11 OPS2_SCHGR OPSIN 2.
12 HBA_HUMAN HEMOGLOBIN ALPHA CHAIN.
13 LACY_ECOLI LACTOSE PERMEASE (LACTOSE-PROTON SYMPORT).
14 FLAV_CLOAB FLAVODOXIN.
15 PAX3_HUMAN PAIRED BOX PROTEIN PAX-3 (HUP2).
16 PAX4_HUMAN PAIRED BOX PROTEIN PAX-4.
17 CO9_FUGRU COMPLEMENT COMPONENT C9 PRECURSOR.
18 SYH_FUGRU HISTIDYL-TRNA SYNTHETASE (EC 6.1.1.21) (HISTIDINE--TRNA LIGASE
19 BGAL_ECOLI BETA-GALACTOSIDASE (EC 3.2.1.23) (LACTASE).
20 HD_FUGRU HUNTINGTIN (HUNTINGTON'S DISEASE PROTEIN HOMOLOG) (HD PROTEIN)
1 : 100K_RAT 1.277e+06 100368.6 0.750
100 KDA PROTEIN (EC 6.3.2.-).
Mw Start End Seq
1086.3 358 367 CATTPMAVHR
1399.6 6 17 GDFLNYALSLMR
2424.7 290 312 VFMEDVGAEPGSILTELGGFEVK
2930.3 671 698 QLILASQSSDADAVFSAMDLAFAVDLCK
3086.3 458 485 QLSIDTRPFRPASEGNPSDDPDPLPAHR
*6082.8 817 870 QDLVYFWTSSPSLPASEEGFQPMPSITIRPPDDQHLPTANTCISR...
No Match 2030.2 5423.0
2 : SYV_FUGRU 3.791e+01 138217.9 0.375
VALYL-TRNA SYNTHETASE (EC 6.1.1.9) (VALINE--TRNA LIGASE) (VALRS).
Mw Start End Seq
1087.3 510 518 TVLHPFCDR
*1399.6 1184 1195 VPVKVQEQDTEK
2425.7 321 343 GETTLWNPGCDHAGIATQVVVEK
No Match 2030.2 2930.3 3086.3 5423.0 6082.8
[Part of this file has been deleted for brevity]
No Match 1399.6 2030.2 2424.7 2930.3 3086.3 5423.0 6082.8
13 : LACY_ECOLI 5.070e+00 46503.1 0.125
LACTOSE PERMEASE (LACTOSE-PROTON SYMPORT).
Mw Start End Seq
*3088.7 290 319 NALLLAGTIMSVRIIGSSFATSALEVVILK
No Match 1086.2 1399.6 2030.2 2424.7 2930.3 5423.0 6082.8
14 : FLAV_CLOAB 4.938e+00 17763.3 0.125
FLAVODOXIN.
Mw Start End Seq
*1085.3 17 26 VAKLIEEGVK
No Match 1399.6 2030.2 2424.7 2930.3 3086.3 5423.0 6082.8
15 : PAX3_HUMAN 3.494e+00 52967.4 0.125
PAIRED BOX PROTEIN PAX-3 (HUP2).
Mw Start End Seq
*2930.3 11 37 MMRPGPGQNYPRSGFPLEVSTPLGQGR
No Match 1086.2 1399.6 2030.2 2424.7 3086.3 5423.0 6082.8
16 : PAX4_HUMAN 3.488e+00 37832.6 0.125
PAIRED BOX PROTEIN PAX-4.
Mw Start End Seq
*2029.4 28 45 QQIVRLAVSGMRPCDISR
No Match 1086.2 1399.6 2424.7 2930.3 3086.3 5423.0 6082.8
17 : CO9_FUGRU 3.007e+00 65197.8 0.125
COMPLEMENT COMPONENT C9 PRECURSOR.
Mw Start End Seq
*2930.2 135 162 TCPPTVLDTNEQGRTAGYGINILGADPR
No Match 1086.2 1399.6 2030.2 2424.7 3086.3 5423.0 6082.8
18 : SYH_FUGRU 2.821e+00 57912.8 0.125
HISTIDYL-TRNA SYNTHETASE (EC 6.1.1.21) (HISTIDINE--TRNA LIGASE) (HISRS).
Mw Start End Seq
1087.2 124 133 DQGGELLSLR
No Match 1399.6 2030.2 2424.7 2930.3 3086.3 5423.0 6082.8
19 : BGAL_ECOLI 2.283e+00 116351.4 0.125
BETA-GALACTOSIDASE (EC 3.2.1.23) (LACTASE).
Mw Start End Seq
1400.6 600 611 QFCMNGLVFADR
No Match 1086.2 2030.2 2424.7 2930.3 3086.3 5423.0 6082.8
20 : HD_FUGRU 2.169e+00 348935.6 0.375
HUNTINGTIN (HUNTINGTON'S DISEASE PROTEIN HOMOLOG) (HD PROTEIN).
Mw Start End Seq
*1400.6 2899 2911 VDGEALVKLSVDR
*2031.3 645 663 LLSASFLLTGQKNGLTPDR
*3085.6 1573 1597 LVQYHQVLEMFILVLQQCHKENEDK
No Match 1086.2 2424.7 2930.3 5423.0 6082.8
|
The emowse search program outputs a listing file containing the following information.
Specified search parameters
Includes all specified parameters such as digest reagent, specified error tolerance, specified intact protein Mw and Mw filter percentage. All supplied peptide Mws are listed in descending order, followed by the total number of entries scanned during the search.Short 'hit' listing
The top 50 scoring proteins are then listed in descending order, details include the sequence ID name and brief text identifiers. Details are limited to the top 50 scores as a deliberate compromise to keep the result listings as short as possible.Detailed 'hit' listing
The top 50 entries are then listed in more detail.The first line includes the sequence ID name, the emowse search score (typically a few powers of 10), the 'hit' protein Mw and finally an 'accuracy' ratio calculated by dividing 'hits' by the total number of peptides used for the search. This cannot be used to ascribe significance, but experience has shown that anything below 0.3 is generally not worth pursuing. Line 2 is the protein text identifier. Subsequent lines list 'hit' and 'miss' peptides, with the appropriate start, end and corresponding sequences of correct peptide matches. 'miss' peptides are indicated by 'No match' at the start of the last line for that protein.Matching peptides marked with a '*' denote partially-cleaved fragments.
Data files
emowse reads in the pre-computed "Frequencies" data file 'Efreqs.dat', (See the section "emowse Scoring scheme", above for a description of the frequency scores.)EMBOSS data files are distributed with the application and stored in the standard EMBOSS data directory, which is defined by the EMBOSS environment variable EMBOSS_DATA.
To see the available EMBOSS data files, run:
% embossdata -showall
To fetch one of the data files (for example 'Exxx.dat') into your current directory for you to inspect or modify, run:
% embossdata -fetch -file Exxx.dat
Users can provide their own data files in their own directories. Project specific files can be put in the current directory, or for tidier directory listings in a subdirectory called ".embossdata". Files for all EMBOSS runs can be put in the user's home directory, or again in a subdirectory called ".embossdata".
The directories are searched in the following order:
- . (your current directory)
- .embossdata (under your current directory)
- ~/ (your home directory)
- ~/.embossdata
Notes
None.References
-
The paper describing the original 'MOWSE' program is:
- D.J.C. Pappin, P. Hojrup and A.J. Bleasby 'Rapid Identification of
Proteins by Peptide-Mass Fingerprinting'. Current Biology (1993), vol
3, 327-332.
Other references:
- Barber M, Bordoli RS, Sedgwick RD, Tyler AN: Fast atom bombardment of solids: a new ion source for mass spectrometry. J Chem Soc Chem Commun 1981, 7: 325-327.
- Dole M, Mack LL, Hines RL, Mobley RC, Ferguson LD, Alice MB: Molecular beams of macroions. J Chem Phys 1968, 49:2240-2249.
- Meng CK, Mann M, Fenn JB: Of protons or proteins. Z Phys D 1988, 10: 361-368.
- Karas M, Hillenkamp F: Laser desorption ionisation of proteins with molecular masses exceeding 10,000 Daltons. Analytical Chemistry 1988, 60:2299-2301.
- Morris H, Panico M, Taylor GW: FAB-mapping of recombinant-DNA protein products. Biochem Biophys Res Commun 1983, 117:299-305.
- Morris H, Greer FM: Mass spectrometry of natural and recombinant proteins and glycoproteins. Trends in Biotechnology 1988, 6:140-147.
- Weissenbach J, Gyapay G, Dib C, Vignal J, Morissette J, Millasseau P, Vaysseix G, Lathrop M: A second generation linkage map of the human genome. Nature 1992, 359:794-801.
- Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH, Xiao H, Merril CR, Wu A, Olde B, Moreno RF, Kerlavage AR, McCombie WR, Venter JC: Complementary DNA sequencing: expressed sequence tags and human genome project. Science 1991, 252:1651-1656.
- Lehrach H, Drmanac R, Hoheisel J, Larin Z, Lennon G, Monaco AP, Nizetic D, Zehetner G, Poustka A: Hybridization fingerprinting in genome mapping and sequencing. In Genome Analysis Volume 1: Genetic and Physical Mapping. Cold Spring Harbor Laboratory Press; 1990:39-81 .
- Akrigg D, Bleasby AJ, Dix NIM, Findlay JBC, North ACT, Parry- Smith D, Wootton JC, Blundell TI, Gardner SP, Hayes F, Sternberg MJE, Thornton JM, Tickle IJ, Murray-Rust P: A protein sequence/structure database. Nature 1988, 335:745-746.
- Bleasby AJ, Wootton JC: Construction of validated, non- redundant composite protein databases. Protein Engineering 1990, 3:153-159.
Warnings
None.Diagnostic Error Messages
None.Exit status
It always exits with status 0.Known bugs
None.See also
| Program name | Description |
|---|---|
| backtranseq | Back translate a protein sequence |
| charge | Protein charge plot |
| checktrans | Reports STOP codons and ORF statistics of a protein |
| compseq | Count composition of dimer/trimer/etc words in a sequence |
| freak | Residue/base frequency table or plot |
| iep | Calculates the isoelectric point of a protein |
| mwcontam | Shows molwts that match across a set of files |
| mwfilter | Filter noisy molwts from mass spec output |
| octanol | Displays protein hydropathy |
| pepinfo | Plots simple amino acid properties in parallel |
| pepstats | Protein statistics |
| pepwindow | Displays protein hydropathy |
| pepwindowall | Displays protein hydropathy of a set of sequences |
Author(s)
Alan Bleasby (ajb © ebi.ac.uk)European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK