CSC

emowse

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Function

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

% 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 [12s1_arath.emowse]: 
ajSeqxrefNewDbS '1-I' 'FT025'

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:6.4.0.0

   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
   -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 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

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
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: 12s1_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.-) (E3 ubiquitin-prote
2   SYVC_TAKRU   Valyl-tRNA synthetase (6.1.1.9) (Valine--tRNA ligase) (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 (eIF-2B GDP-
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   Histidyl-tRNA synthetase, cytoplasmic (6.1.1.21) (Histidine--t
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       1.233e+06 103949.9   0.750 
         E3 ubiquitin-protein ligase UBR5 (6.3.2.-) (E3 ubiquitin-protein ligase, HECT domain-containing 1) (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                 
        *6082.9 848    901    QDLVYFWTSSPSLPASEEGFQPMPSITIRPPDDQHLPTANTCISR...
         No Match      2030.2 5423.0 

    2  : SYVC_TAKRU     3.791e+01 138218.2   0.375 
         Valyl-tRNA synthetase (6.1.1.9) (Valine--tRNA ligase) (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 
         Histidyl-tRNA synthetase, cytoplasmic (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 

    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:

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



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
Reports on protein proteolytic enzyme or reagent cleavage sites



pepinfo
Plot amino acid properties of a protein sequence in parallel



pepstats
Calculates 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 © 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