
                                  emowse 



Function

   Protein identification by mass spectrometry

Description

   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 (Trypsin)
   2 (Lys-C)
   3 (Arg-C)
   4 (Asp-N)
   5 (V8-bicarb)
   6 (V8-phosph)
   7 (Chymotrypsin)
   8 (CNBr)
   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.


CAPTION: Prefixes to use with sequence information for
emowse


 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 example
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.

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:
    1. 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:
    2. 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.
    3. Dole M, Mack LL, Hines RL, Mobley RC, Ferguson LD, Alice MB:
       Molecular beams of macroions. J Chem Phys 1968, 49:2240-2249.
    4. Meng CK, Mann M, Fenn JB: Of protons or proteins. Z Phys D 1988,
       10: 361-368.
    5. Karas M, Hillenkamp F: Laser desorption ionisation of proteins
       with molecular masses exceeding 10,000 Daltons. Analytical
       Chemistry 1988, 60:2299-2301.
    6. Morris H, Panico M, Taylor GW: FAB-mapping of recombinant-DNA
       protein products. Biochem Biophys Res Commun 1983, 117:299-305.
    7. Morris H, Greer FM: Mass spectrometry of natural and recombinant
       proteins and glycoproteins. Trends in Biotechnology 1988,
       6:140-147.
    8. 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.
    9. 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.
   10. 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 .
   11. 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.
   12. 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

History

   Written (Sept 2000) - Alan Bleasby.

Target users

   This program is intended to be used by everyone and everything, from
   naive users to embedded scripts.

Comments

   None
