EMBOSS: emowse

| emowse

## Function

Search protein sequences by digest fragment molecular weight

## Description

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).

## Usage

Here is a sample session with emowse

    % emowse
    Search protein sequences by digest fragment molecular weight
    Input protein sequence(s): tsw:*
    Peptide molecular weight values file: test.mowse
    Whole sequence molwt [0]:
    Use monoisotopic weights [N]:
    Output file [edd1_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     Protein sequence(s) filename and optional
                                      format, or reference (input USA)
      [-infile]            infile     Peptide molecular weight values file
       -weight             integer    [0] Whole sequence molwt (Any integer value)
       -mono               boolean    [N] Use monoisotopic weights
      [-outfile]           outfile    [*.emowse] Output file name

       Additional (Optional) qualifiers: (none)
       Advanced (Unprompted) qualifiers:
       -mwdata             datafile   [Emolwt.dat] Molecular weights data file
       -frequencies        datafile   [Efreqs.dat] Amino acid frequencies data
                                      file
       -enzyme             menu       [1] Enzyme or reagent (Values: 1 (Trypsin);
                                      2 (Lys-C); 3 (Arg-C); 4 (Asp-N); 5
                                      (V8-bicarb); 6 (V8-phosph); 7
                                      (Chymotrypsin); 8 (CNBr))
       -pcrange            integer    [25] Allowed whole sequence weight
                                      variability (Integer from 0 to 75)
       -tolerance          float      [0.1] Tolerance (Number from 0.100 to 1.000)
       -partials           float      [0.4] Partials factor (Number from 0.100 to
                                      1.000)

       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 first file to standard output
       -filter             boolean    Read first file from standard input, write
                                      first file to 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 dying program messages

Standard (Mandatory) qualifiers | Allowed values | Default
--- | --- | ---
[-sequence] (Parameter 1) | Protein sequence(s) filename and optional format, or reference (input USA) | Readable sequence(s) | Required
[-infile] (Parameter 2) | Peptide molecular weight values file | Input file | Required
-weight | Whole sequence molwt | Any integer value | 0
-mono | Use monoisotopic weights | Boolean value Yes/No | No
[-outfile] (Parameter 3) | Output file name | Output file | <*>.emowse
Additional (Optional) qualifiers | Allowed values | Default
(none)
Advanced (Unprompted) qualifiers | Allowed values | Default
-mwdata | Molecular weights data file | Data file | Emolwt.dat
-frequencies | Amino acid frequencies data 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.

    Prefixes to use with sequence information for
    emowse
    PrefixMeaningExample
    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: edd1_rat.emowse

    Using data fragments of:
              1086.2
              1399.6
              2030.2
              2424.7
              2930.3
              3086.3
              5423.0
              6082.8

    1   EDD1_RAT     Ubiquitin-protein ligase EDD1 (EC 6.3.2.-) (Hyperplastic discs
    2   SYV_FUGRU    Valyl-tRNA synthetase (EC 6.1.1.9) (Valine--tRNA ligase) (ValR
    3   TCPD_FUGRU   T-complex protein 1 subunit delta (TCP-1-delta) (CCT-delta).
    4   OPS2_DROME   Opsin Rh2 (Ocellar opsin).
    5   FLAV_ECO57   Flavodoxin-1.
    6   FLAV_ECOL6   Flavodoxin-1.
    7   FLAV_ECOLI   Flavodoxin-1.
    8   FLAV_KLEPN   Flavodoxin.
    9   FLAV_SYNY3   Flavodoxin.
    10  EI2BB_FUGRU  Translation initiation factor eIF-2B subunit beta (eIF-2B GDP-
    11  FLAV_HAEIN   Flavodoxin.
    12  HIRA_FUGRU   HIRA protein (TUP1-like enhancer of split protein 1).
    13  OPS2_SCHGR   Opsin-2.
    14  HBA_HUMAN    Hemoglobin subunit alpha (Hemoglobin alpha chain) (Alpha-globi
    15  HBA_PANPA    Hemoglobin subunit alpha (Hemoglobin alpha chain) (Alpha-globi
    16  HBA_PANTR    Hemoglobin subunit alpha (Hemoglobin alpha chain) (Alpha-globi
    17  LACY_ECOLI   Lactose permease (Lactose-proton symport).
    18  FLAV_CLOSA   Flavodoxin.
    19  AMIC_PSEAE   Aliphatic amidase expression-regulating protein.
    20  PAX3_HUMAN   Paired box protein Pax-3 (HUP2).
    21  PAX4_HUMAN   Paired box protein Pax-4.
    22  CO9_FUGRU    Complement component C9 precursor.
    23  SYH_FUGRU    Histidyl-tRNA synthetase (EC 6.1.1.21) (Histidine--tRNA ligase
    24  BGAL_ECOLI   Beta-galactosidase (EC 3.2.1.23) (Lactase).
    25  HD_FUGRU     Huntingtin (Huntington disease protein homolog) (HD protein).

        1  : EDD1_RAT       1.233e+06 103949.6   0.750
             Ubiquitin-protein ligase EDD1 (EC 6.3.2.-) (Hyperplastic discs protein homolog) (100 kDa protein) (Fragment).
             Mw     Start  End    Seq
             1086.3 389    398    CATTPMAVHR
             1399.6 37     48     GDFLNYALSLMR
             2424.7 321    343    VFMEDVGAEPGSILTELGGFEVK
             2930.3 702    729    QLILASQSSDADAVFSAMDLAFAVDLCK
             3086.3 489    516    QLSIDTRPFRPASEGNPSDDPDPLPAHR
            *6082.8 848    901    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).

      [Part of this file has been deleted for brevity]

             No Match      1086.2 1399.6 2030.2 2424.7 2930.3 5423.0 6082.8

        18 : FLAV_CLOSA     4.938e+00 17763.4    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

        19 : AMIC_PSEAE     3.859e+00 42807.0    0.125
             Aliphatic amidase expression-regulating protein.
             Mw     Start  End    Seq
            *2423.7 308    328    VEDVQRHLYDICIDAPQGPVR
             No Match      1086.2 1399.6 2030.2 2930.3 3086.3 5423.0 6082.8

        20 : 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

        21 : 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

        22 : 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

        23 : 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

        24 : BGAL_ECOLI     2.280e+00 116482.5   0.125
             Beta-galactosidase (EC 3.2.1.23) (Lactase).
             Mw     Start  End    Seq
             1400.6 601    612    QFCMNGLVFADR
             No Match      1086.2 2030.2 2424.7 2930.3 3086.3 5423.0 6082.8

        25 : HD_FUGRU       2.169e+00 348935.6   0.375
             Huntingtin (Huntington 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

    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.

        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

    backtranambig
    Back-translate a protein sequence to ambiguous nucleotide sequence

    backtranseq
    Back-translate a protein sequence to a nucleotide sequence

    charge
    Draw a protein charge plot

    checktrans
    Reports STOP codons and ORF statistics of a protein

    compseq
    Calculate the composition of unique words in sequences

    freak
    Generate residue/base frequency table or plot

    iep
    Calculate the isoelectric point of proteins

    mwcontam
    Find weights common to multiple molecular weights files

    mwfilter
    Filter noisy data from molecular weights file

    octanol
    Draw a White-Wimley protein hydropathy plot

    pepinfo
    Plot amino acid properties of a protein sequence in parallel

    pepstats
    Calculates statistics of protein properties

    pepwindow
    Draw a Kyte-Doolittle hydropathy plot for a protein sequence

    pepwindowall
    Draw Kyte-Doolittle hydropathy plot for a protein alignment

    wordcount
    Count and extract unique words in DNA sequence(s)

        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