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<HTML>
<HEAD>
<TITLE>
EMBOSS: emowse
</TITLE>
</HEAD>
<BODY BGCOLOR="#FFFFFF" text="#000000">
<table align=center border=0 cellspacing=0 cellpadding=0>
<tr><td valign=top>
<A HREF="/" ONMOUSEOVER="self.status='Go to the EMBOSS home page';return true"><img border=0 src="emboss_icon.jpg" alt="" width=150 height=48></a>
</td>
<td align=left valign=middle>
<b><font size="+6">
emowse
</font></b>
</td></tr>
</table>
<br>
<p>
<H2>
Function
</H2>
Protein identification by mass spectrometry
<H2>
Description
</H2>
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.
<p>
Given a one-per-line file of molecular weights cut by enzymes/reagents,
<b>emowse</b> will search a protein database for matches with the mass
spectrometry data.
<p>
One of eight cutting enzymes/reagents can be specified and an optional whole
sequence molecular weight.
<p>
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.
<p>
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].
<p>
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.
<p>
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].
<p>
For each entry in the specified set of sequences to search,
<b>emowse</b> 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:
<p>
<pre>
N
__
\
/ Residue mass + 18.0153
--
n=1
</pre>
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.
<p>
<h3>Table 1: Cleavage reagents modelled by <b>emowse</b>.</h3>
<pre>
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
</pre>
<p>
Current versions of <b>emowse</b> 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.
<p>
<h3>Tolerance</h3>
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.
<h3>Whole sequence molecular weight</h3>
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 <b>emowse</b> 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'.
<h3>Allowed whole sequence weight variability</h3>
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.
<h3>Partials factor</h3>
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.
<p>
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.
<p>
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.
<h3>Program requirements</h3>
The <b>emowse</b> 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).
<p>
For each peptide Mw entry in the data file, <b>emowse</b> matches
individual fragment molecular weights (FMWs) with database entry
molecular weights (DBMWs). A 'hit' is scored when the following
criterion is met:
<p>
<pre>
DBMW-tolerance-1 < FMW < DBMW+tolerance+1
</pre>
<p>
If an intact protein Mw is specified (SMW) then the program
prompts for a molecular weight filter percentage (MWFP). <b>emowse</b>
then restricts the search to those entries which match the
following criteria:
<p>
<pre>
R = SMW x MWFP / 100
0 < SMW-R < <b>emowse</b> entry Mol.wt. < SMW+R
</pre>
<p>
Default search parameters are a tolerance of +/- 2 Daltons,
intact Mw specified and the MWFP set to 25.
<h3><b>emowse</b> Scoring scheme</h3>
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:
<p>
<pre>
Cell value = Old value / Fmax
</pre>
<p>
to yield floating point numbers between 0 and 1. These
distribution frequency values, calculated for each cleavage
reagent, were then built into the <b>emowse</b> 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:
<p>
<pre>
Score = 50/(Pn x H)
</pre>
<p>
Where Pn is the product of n distribution scores and H the 'hit'
protein molecular weight in kD.
<p>
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.
<h3>Simulation studies</h3>
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.
<p>
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.
<H2>
Usage
</H2>
<b>Here is a sample session with emowse</b>
<p>
<p>
<table width="90%"><tr><td bgcolor="#CCFFFF"><pre>
% <b>emowse </b>
Protein identification by mass spectrometry
Input protein sequence(s): <b>tsw:*</b>
Peptide molecular weight values file: <b>test.mowse</b>
Whole sequence molwt [0]: <b></b>
Output file [edd1_rat.emowse]: <b></b>
</pre></td></tr></table><p>
<p>
<a href="#input.1">Go to the input files for this example</a><br><a href="#output.1">Go to the output files for this example</a><p><p>
<H2>
Command line arguments
</H2>
<table CELLSPACING=0 CELLPADDING=3 BGCOLOR="#f5f5ff" ><tr><td>
<pre>
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)
[-outfile] outfile [*.emowse] Output file name
Additional (Optional) qualifiers: (none)
Advanced (Unprompted) qualifiers:
-aadata datafile [Eamino.dat] Amino acids properties and
molecular weight 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 standard output
-filter boolean Read standard input, write standard output
-options boolean Prompt for standard and additional values
-debug boolean Write debug output to program.dbg
-verbose boolean Report some/full command line options
-help boolean Report command line options. More
information on associated and general
qualifiers can be found with -help -verbose
-warning boolean Report warnings
-error boolean Report errors
-fatal boolean Report fatal errors
-die boolean Report dying program messages
</pre>
</td></tr></table>
<P>
<table border cellspacing=0 cellpadding=3 bgcolor="#ccccff">
<tr bgcolor="#FFFFCC">
<th align="left" colspan=2>Standard (Mandatory) qualifiers</th>
<th align="left">Allowed values</th>
<th align="left">Default</th>
</tr>
<tr>
<td>[-sequence]<br>(Parameter 1)</td>
<td>Protein sequence(s) filename and optional format, or reference (input USA)</td>
<td>Readable sequence(s)</td>
<td><b>Required</b></td>
</tr>
<tr>
<td>[-infile]<br>(Parameter 2)</td>
<td>Peptide molecular weight values file</td>
<td>Input file</td>
<td><b>Required</b></td>
</tr>
<tr>
<td>-weight</td>
<td>Whole sequence molwt</td>
<td>Any integer value</td>
<td>0</td>
</tr>
<tr>
<td>[-outfile]<br>(Parameter 3)</td>
<td>Output file name</td>
<td>Output file</td>
<td><i><*></i>.emowse</td>
</tr>
<tr bgcolor="#FFFFCC">
<th align="left" colspan=2>Additional (Optional) qualifiers</th>
<th align="left">Allowed values</th>
<th align="left">Default</th>
</tr>
<tr>
<td colspan=4>(none)</td>
</tr>
<tr bgcolor="#FFFFCC">
<th align="left" colspan=2>Advanced (Unprompted) qualifiers</th>
<th align="left">Allowed values</th>
<th align="left">Default</th>
</tr>
<tr>
<td>-aadata</td>
<td>Amino acids properties and molecular weight data file</td>
<td>Data file</td>
<td>Eamino.dat</td>
</tr>
<tr>
<td>-frequencies</td>
<td>Amino acid frequencies data file</td>
<td>Data file</td>
<td>Efreqs.dat</td>
</tr>
<tr>
<td>-enzyme</td>
<td>Enzyme or reagent</td>
<td><table><tr><td>1</td> <td><i>(Trypsin)</i></td></tr><tr><td>2</td> <td><i>(Lys-C)</i></td></tr><tr><td>3</td> <td><i>(Arg-C)</i></td></tr><tr><td>4</td> <td><i>(Asp-N)</i></td></tr><tr><td>5</td> <td><i>(V8-bicarb)</i></td></tr><tr><td>6</td> <td><i>(V8-phosph)</i></td></tr><tr><td>7</td> <td><i>(Chymotrypsin)</i></td></tr><tr><td>8</td> <td><i>(CNBr)</i></td></tr></table></td>
<td>1</td>
</tr>
<tr>
<td>-pcrange</td>
<td>Allowed whole sequence weight variability</td>
<td>Integer from 0 to 75</td>
<td>25</td>
</tr>
<tr>
<td>-tolerance</td>
<td>Tolerance</td>
<td>Number from 0.100 to 1.000</td>
<td>0.1</td>
</tr>
<tr>
<td>-partials</td>
<td>Partials factor</td>
<td>Number from 0.100 to 1.000</td>
<td>0.4</td>
</tr>
</table>
<H2>
Input file format
</H2>
<a name="input.1"></a>
<h3>Input files for usage example </h3>
'tsw:*' is a sequence entry in the example protein database 'tsw'
<p>
<p><h3>File: test.mowse</h3>
<table width="90%"><tr><td bgcolor="#FFCCFF">
<pre>
6082.8
5423.0
3086.3
2930.3
2424.7
2030.2
1399.6
1086.2
</pre>
</td></tr></table><p>
<p>
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
<b>digest</b>).
<p>
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.
<p>
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.
<p>
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.
<p>
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:
<p>
<pre>
mw seq(...) comp(...)
</pre>
<p>
where mw is the molecular mass of the fragment,
<TT>seq(...)</TT> is sequence information and
<TT>comp(...)</TT> is composition information.
A line may contain more than one sequence information qualifiers.
For example:
<p>
<hr>
<pre>
7176 seq(b-t[pqt]ln)
1744
1490
1433 comp(3[ed]1[p]) seq(gmde)
<pre>
<hr>
<p>
<H3>Sequence information</H3>
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.
<p>
<Table border="1">
<Caption><B>Prefixes to use with sequence information for
<b>emowse</b></B></caption>
<TR align="center"><TH>Prefix</TH><TH>Meaning</TH><TH>Example</TH></TR>
<TR align="center"><TD><TT>b-</TT></TD><TD>N->C sequence</TD>
<TD><TT>seq(b-DEFG)</TT></TD></TR>
<TR align="center"><TD><TT>y-</TT></TD><TD>C->N sequence</TD>
<TD><TT>seq(y-GFED)</TT></TD></TR>
<TR align="center"><TD><TT>*-</TT></TD>
<TD>Orientation unknown</TD>
<TD><TT>seq(*-DEFG)<br>seq(*-GFED)</TT></TD></TR>
<TR align="center"><TD><TT>n-</TT></TD><TD>N terminal sequence</TD>
<TD><TT>seq(n-ACDE)</TT></TD></TR>
<TR align="center"><TD><TT>c-</TT></TD><TD>C terminal sequence</TD>
<TD><TT>seq(c-FGHI)</TT></TD></TR>
<TR align="center"><TD colspan=3>The examples are all correct data for a
peptide with a sequence ACDEFGHI.<BR> Note that *-DEFG
will search for both DEFG and GFED</TD></TR>
</TABLE>
<p>
Both lower and upper case characters may be used for amino-acids.
An unknown amino acid may be indicated by an '<TT>X</TT>'.
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:
<pre>
12345 seq(n-AC[DE]) seq(c-HI)
</pre>
<H3>Composition Information</H3>
Composition should consist of a number, followed by the
corresponding amino acid between square brackets.
For example
<PRE>comp(2[H]0[M]3[DE]*[K])</PRE> indicates
a peptide which contains 2 histidines, no methionines,
3 acidic residues (glutamic or aspartic acid) and
at least 1 lysine.
<p>
<H2>
Output file format
</H2>
<a name="output.1"></a>
<h3>Output files for usage example </h3>
<p><h3>File: edd1_rat.emowse</h3>
<table width="90%"><tr><td bgcolor="#CCFFCC">
<pre>
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).
<font color=red> [Part of this file has been deleted for brevity]</font>
No Match 1086.2 1399.6 2030.2 2424.7 2930.3 5423.0 6082.8
18 : FLAV_CLOSA 4.938e+00 17763.3 0.125
Flavodoxin.
Mw Start End Seq
*1085.3 17 26 VAKLIEEGVK
No Match 1399.6 2030.2 2424.7 2930.3 3086.3 5423.0 6082.8
19 : AMIC_PSEAE 3.859e+00 42806.9 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
</pre>
</td></tr></table><p>
<p>
The <b>emowse</b> search program outputs a listing file containing the
following information.
<p>
<h3>Specified search parameters</h3>
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.
<h3>Short 'hit' listing</h3>
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.
<h3>Detailed 'hit' listing</h3>
The top 50 entries are then listed in more detail.The first
line includes the sequence ID name, the <b>emowse</b> 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.
<p>
Matching peptides marked with a '*' denote partially-cleaved
fragments.
<p>
<H2>
Data files
</H2>
<b>emowse</b> reads in the pre-computed "Frequencies" data file
'Efreqs.dat', (See the section "<b>emowse</b> Scoring scheme", above for
a description of the frequency scores.)
<p>
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.
<p>
To see the available EMBOSS data files, run:
<p>
<pre>
% embossdata -showall
</pre>
<p>
To fetch one of the data files (for example 'Exxx.dat') into your
current directory for you to inspect or modify, run:
<pre>
% embossdata -fetch -file Exxx.dat
</pre>
<p>
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".
<p>
The directories are searched in the following order:
<ul>
<li> . (your current directory)
<li> .embossdata (under your current directory)
<li> ~/ (your home directory)
<li> ~/.embossdata
</ul>
<p>
<H2>
Notes
</H2>
None.
<H2>
References
</H2>
<ol>
The paper describing the original 'MOWSE' program is:
<li>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.
<p>Other references:
<LI>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.
<LI>Dole M, Mack LL, Hines RL, Mobley RC, Ferguson LD, Alice MB:
Molecular beams of macroions. J Chem Phys 1968, 49:2240-2249.
<LI>Meng CK, Mann M, Fenn JB: Of protons or proteins. Z Phys D
1988, 10: 361-368.
<LI>Karas M, Hillenkamp F: Laser desorption ionisation of proteins
with molecular masses exceeding 10,000 Daltons. Analytical
Chemistry 1988, 60:2299-2301.
<LI>Morris H, Panico M, Taylor GW: FAB-mapping of recombinant-DNA
protein products. Biochem Biophys Res Commun 1983, 117:299-305.
<LI>Morris H, Greer FM: Mass spectrometry of natural and
recombinant proteins and glycoproteins. Trends in Biotechnology
1988, 6:140-147.
<LI>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.
<LI>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.
<LI>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 .
<LI>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.
<LI>Bleasby AJ, Wootton JC: Construction of validated, non-
redundant composite protein databases. Protein Engineering 1990,
3:153-159.
</ol>
<H2>
Warnings
</H2>
None.
<H2>
Diagnostic Error Messages
</H2>
None.
<H2>
Exit status
</H2>
It always exits with status 0.
<H2>
Known bugs
</H2>
None.
<h2><a name="See also">See also</a></h2>
<table border cellpadding=4 bgcolor="#FFFFF0">
<tr><th>Program name</th><th>Description</th></tr>
<tr>
<td><a href="backtranambig.html">backtranambig</a></td>
<td>Back translate a protein sequence to ambiguous codons</td>
</tr>