                                   emowse



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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 [cru4_arath.emowse]:


   Go to the input files for this example
   Go to the output files for this example

Command line arguments

Search protein sequences by digest fragment molecular weight
Version: EMBOSS:6.6.0.0

   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
   -scircular1         boolean    Sequence is circular
   -squick1            boolean    Read id and sequence only
   -sformat1           string     Input sequence format
   -iquery1            string     Input query fields or ID list
   -ioffset1           integer    Input start position offset
   -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 and exit. 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
   -version            boolean    Report version number and exit


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: cru4_arath.emowse


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

1   UBR5_RAT     E3 ubiquitin-protein ligase UBR5 (6.3.2.-) (100 kDa protein) (
2   SYVC_TAKRU   Valine--tRNA ligase (6.1.1.9) (Valyl-tRNA synthetase) (ValRS)
3   TCPD_TAKRU   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_TAKRU  Translation initiation factor eIF-2B subunit beta (S20I15) (eI
11  FLAV_HAEIN   Flavodoxin
12  HIRA_TAKRU   Protein HIRA (TUP1-like enhancer of split protein 1)
13  OPS2_SCHGR   Opsin-2
14  LACY_ECOLI   Lactose permease (Lactose-proton symport)
15  FLAV_CLOSA   Flavodoxin
16  AMIC_PSEAE   Aliphatic amidase expression-regulating protein
17  PAX3_HUMAN   Paired box protein Pax-3 (HuP2)
18  PAX4_HUMAN   Paired box protein Pax-4
19  CO9_TAKRU    Complement component C9 (Precursor)
20  SYHC_TAKRU   Histidine--tRNA ligase, cytoplasmic (6.1.1.21) (Histidyl-tRNA
21  BGAL_ECOLI   Beta-galactosidase (Beta-gal) (3.2.1.23) (Lactase)
22  HD_TAKRU     Huntingtin (Huntington disease protein homolog) (HD protein ho

    1  : UBR5_RAT       1.212e+05 308026.9   0.750
         E3 ubiquitin-protein ligase UBR5 (6.3.2.-) (100 kDa protein) (E3 ubiqui
tin-protein ligase, HECT domain-containing 1) (Hyperplastic discs protein homolo
g)
         Mw     Start  End    Seq
         1086.3 2257   2266   CATTPMAVHR
         1399.6 1905   1916   GDFLNYALSLMR
         2424.7 2189   2211   VFMEDVGAEPGSILTELGGFEVK
         2930.3 2570   2597   QLILASQSSDADAVFSAMDLAFAVDLCK
         3086.3 2357   2384   QLSIDTRPFRPASEGNPSDDPDPLPAHR
        *6082.9 2716   2769   QDLVYFWTSSPSLPASEEGFQPMPSITIRPPDDQHLPTANTCISR...
         No Match      2030.2 5423.0

    2  : SYVC_TAKRU     3.791e+01 138218.2   0.375
         Valine--tRNA ligase (6.1.1.9) (Valyl-tRNA synthetase) (ValRS)
         Mw     Start  End    Seq
         1087.3 510    518    TVLHPFCDR
        *1399.6 1184   1195   VPVKVQEQDTEK


  [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

    15 : 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

    16 : AMIC_PSEAE     3.859e+00 42807.1    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

    17 : PAX3_HUMAN     3.494e+00 52967.5    0.125
         Paired box protein Pax-3 (HuP2)
         Mw     Start  End    Seq
        *2930.4 11     37     MMRPGPGQNYPRSGFPLEVSTPLGQGR
         No Match      1086.2 1399.6 2030.2 2424.7 3086.3 5423.0 6082.8

    18 : PAX4_HUMAN     3.488e+00 37832.7    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

    19 : CO9_TAKRU      3.007e+00 65197.9    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

    20 : SYHC_TAKRU     2.821e+00 57913.0    0.125
         Histidine--tRNA ligase, cytoplasmic (6.1.1.21) (Histidyl-tRNA synthetas
e) (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

    21 : BGAL_ECOLI     2.280e+00 116482.9   0.125
         Beta-galactosidase (Beta-gal) (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

    22 : HD_TAKRU       2.169e+00 348936.6   0.375
         Huntingtin (Huntington disease protein homolog) (HD protein homolog)
         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:
    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
   backtranambig    Back-translate a protein sequence to ambiguous nucleotide
                    sequence
   backtranseq      Back-translate a protein sequence to a nucleotide sequence
   compseq          Calculate the composition of unique words in sequences
   freak            Generate residue/base frequency table or plot
   mwcontam         Find weights common to multiple molecular weights files
   mwfilter         Filter noisy data from molecular weights file
   oddcomp          Identify proteins with specified sequence word composition
   pepdigest        Report on protein proteolytic enzyme or reagent cleavage
                    sites
   pepinfo          Plot amino acid properties of a protein sequence in parallel
   pepstats         Calculate statistics of protein properties
   wordcount        Count and extract unique words in molecular sequence(s)

Author(s)

   Alan Bleasby
   European Bioinformatics Institute, Wellcome Trust Genome Campus,
   Hinxton, Cambridge CB10 1SD, UK

   Please report all bugs to the EMBOSS bug team
   (emboss-bug (c) emboss.open-bio.org) not to the original author.

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
