emowse.txt

emboss的linux版本的源代码

                                  emowse 



Function

   Protein identification by mass spectrometry

Description

   Peptide mass information can provide a 'fingerprint' signature
   sufficiently discriminating to allow for the unique and rapid
   identification of unknown sample proteins, independent of other
   analytical methods such as protein sequence analysis. Practical
   experience has shown that sample proteins can be uniquely identified
   using as few as 3-4 experimentally determined peptide masses when
   screened against a fragment database derived from over 50,000
   proteins.

   Given a one-per-line file of molecular weights cut by
   enzymes/reagents, emowse will search a protein database for matches
   with the mass spectrometry data.

   One of eight cutting enzymes/reagents can be specified and an optional
   whole sequence molecular weight.

   Determination of molecular weight has always been an important aspect
   of the characterization of biological molecules. Protein molecular
   weight data, historically obtained by SDS gel electrophoresis or gel
   permeation chromatography, has been used establish purity, detect
   post-translational modification (such as phosphorylation or
   glycosylation) and aid identification. Until just over a decade ago,
   mass spectrometric techniques were typically limited to relatively
   small biomolecules, as proteins and nucleic acids were too large and
   fragile to withstand the harsh physical processes required to induce
   ionization. This began to change with the development of 'soft'
   ionization methods such as fast atom bombardment (FAB)[1],
   electrospray ionisation (ESI) [2,3] and matrix-assisted laser
   desorption ionisation (MALDI)[4], which can effect the efficient
   transition of large macromolecules from solution or solid crystalline
   state into intact, naked molecular ions in the gas phase. As an added
   bonus to the protein chemist, sample handling requirements are minimal
   and the amounts required for MS analysis are in the same range, or
   less, than existing analytical methods.

   As well as providing accurate mass information for intact proteins,
   such techniques have been routinely used to produce accurate peptide
   molecular weight 'fingerprint' maps following digestion of known
   proteins with specific proteases. Such maps have been used to confirm
   protein sequences (allowing the detection of errors of translation,
   mutation or insertion), characterise post-translational modifications
   or processing events and assign disulphide bonds [5,6].

   Less well appreciated, however, is the extent to which such peptide
   mass information can provide a 'fingerprint' signature sufficiently
   discriminating to allow for the unique and rapid identification of
   unknown sample proteins, independent of other analytical methods such
   as protein sequence analysis.

   Practical experience has shown that sample proteins can be uniquely
   identified using as few as 3- 4 experimentally determined peptide
   masses when screened against a fragment database derived from over
   50,000 proteins. Experimental errors of a few Daltons are tolerated by
   the scoring algorithms, permitting the use of inexpensive
   time-of-flight mass spectrometers. As with other types of physical
   data, such as amino acid composition or linear sequence, peptide
   masses can clearly provide a set of determinants sufficiently unique
   to identify or match unknown sample proteins. Peptide mass
   fingerprints can prove as discriminating as linear peptide sequence,
   but can be obtained in a fraction of the time using less material. In
   many cases, this allows for a rapid identification of a sample protein
   before committing to protein sequence analysis. Fragment masses also
   provide structural information, at the protein level, fully
   complementary to large-scale DNA sequencing or mapping projects
   [7,8,9].

   For each entry in the specified set of sequences to search, emowse
   derives both whole sequence molecular weight and calculated peptide
   molecular weights for complete digests using the range of cleavage
   reagents and rules detailed in Table 1. Cleavage is disallowed if the
   target residue is followed by proline (except for CNBr or Asp N). Glu
   C (S. aureus V8 protease) cleavages are also inhibited if the adjacent
   residue is glutamic acid. Peptide mass calculations are based entirely
   on the linear sequence and use the average isotopic masses of
   amide-bonded amino acid residues (IUPAC 1987 relative atomic masses).
   To allow for N-terminal hydrogen and C-terminal hydroxyl the final
   calculated molecular weight of a peptide of N residues is given by the
   equation:

        N
        __
        \
        /  Residue mass + 18.0153
        --
        n=1

   Molecular weights are rounded to the nearest integer value before
   being used. Cysteine residues are calculated as the free thiol,
   anticipating that samples are reduced prior to mass analysis. CNBr
   fragments are calculated as the homoserine lactone form. Information
   relating to post- translational modification (phosphorylation,
   glycosylation etc.) is not incorporated into calculation of peptide
   masses.

  Table 1: Cleavage reagents modelled by emowse.

Reagent no.     Reagent                 Cleavage rule

        1       Trypsin                 C-term to K/R
        2       Lys-C                   C-term to K
        3       Arg-C                   C-term to R
        4       Asp-N                   N-term to D
        5       V8-bicarb               C-term to E
        6       V8-phosph               C-term to E/D
        7       Chymotrypsin            C-term to F/W/Y/L/M
        8       CNBr                    C-term to M

   Current versions of emowse also incorporate calculated peptide Mw's
   resulting from incomplete or partial cleavages. At present, this is
   achieved by computing all nearest-neighbour pairs for each enzyme or
   reagent detailed in table 1.

  Tolerance

   The supplied number specifies the error allowed for mass accuracy of
   experimental mass determination. If no figure is specified, a default
   tolerance of 2 Daltons will be assumed. If you wish to specify a
   different tolerance then follow the qualifier '-tolerance' with the
   required number of Daltons. eg: '-tolerance 1'. In this case, supplied
   peptide masses will be matched to +/- 1 Daltons. Values of 2-4 are
   suggested for data obtained by laser- desorption TOF instruments.
   Accuracies of +/- 2 Daltons or better are generally only possible
   using an appropriate internal standard (e.g. oxidised insulin B chain)
   with TOF instruments. For electrospray or FAB data, a value of 1 can
   be selected in most cases. If you have real confidence in mass
   determination, specify '0' (zero) to limit matches to the nearest
   integer value (effectively +/- 0.5 Daltons). Discrimination is
   significantly improved by the selection of a small error tolerance.

  Whole sequence molecular weight

   This option allows you to give the molwt of the whole protein (if
   known). This allows you to limit the search to proteins of this molwt
   plus/minus a 'limit' (see below). If unspecified, a whole protein
   molwt of 0 is assumed which emowse interprets as "search the whole
   database". This will include all proteins up to the maximum size of
   just under 700,000 Daltons. You can specify any molwt in Daltons with
   this command e.g. '-weight 90000'.

  Allowed whole sequence weight variability

   This option is used in conjunction with the '-weight' option and is
   meaningless without it. It specifies a percentage. Only proteins of
   the given Sequence molecular weight +/- this percentage will be
   searched. If a Sequence molecular weight is specified but '-pcrange'
   is unspecified then '-pcrange ' will default to 25%. To specify a
   percentage of 30% use: '-pcrange 30'. In this case, a molecular weight
   of 90,000 Daltons was specified and the selection of 30 for the filter
   restricts the search to those proteins with masses from 63,000 to
   117,000 Daltons. A value of 25 is suggested for initial searches,
   which can be progressively widened for subsequent search attempts if
   no matches are found. Discrimination is best when the filter
   percentage is narrow, but some Mw estimates (particularly from SDS
   gels) should be given considerable allowance for error.

  Partials factor

   This specifies the weighting given to partially-cleaved peptide
   fragments, with a range from 0.1 to 1.0. If not specified, the default
   value is 0.4. The factor effectively down-weights the score awarded to
   a partial fragment by the specified amount. For example, a '-partials'
   of 0.25 will reduce the score of partial fragments to 25% (one
   quarter) of the score of a complete ('perfect') peptide cleavage
   fragment of equal mass.

   Computing all possible nearest-neighbour partial fragments adds
   significantly to the number of peptides entered in the database (by a
   factor of two). The major effect of this is to increase the background
   score by increasing the number of random Mw matches, which can
   significantly reduce discrimination. The use of a low '-partials'
   factor (eg 0.1 - 0.3) is a useful way of limiting this effect -
   partial peptide matches will add a little to the cumulative frequency
   score, but without compromising discrimination.

   More experienced users can utilise the '-partials' factor to optimize
   searches where the peptide Mw data contain a significant proportion of
   partial cleavage fragments (eg > 30%). In such cases, setting the
   '-partials' factor within the range 0.4 - 0.6 can help to improve
   discrimination. Conversely, if the digestion is perfect, with no
   partial fragments present, the lowest '-partials' factor of 0.1 will
   give maximum discrimination.

  Program requirements

   The emowse search program accepts a single text file containing a list
   of experimentally-determined masses, generally selected from the range
   700-4,000 Daltons to reduce the influence of partial cleavage
   products. The program outputs a ranked hit list comprising the top 30
   scores, with information including the protein entry name, text
   identifiers, final accumulated scores, matching peptide sequences and
   hit versus miss tallies. User-selectable search parameters include an
   error tolerance (default +/- 2 Daltons), selection of the enzyme or
   reagent used and an intact protein Mw (optional, if known).

   For each peptide Mw entry in the data file, emowse matches individual
   fragment molecular weights (FMWs) with database entry molecular
   weights (DBMWs). A 'hit' is scored when the following criterion is
   met:

        DBMW-tolerance-1 < FMW < DBMW+tolerance+1

   If an intact protein Mw is specified (SMW) then the program prompts
   for a molecular weight filter percentage (MWFP). emowse then restricts
   the search to those entries which match the following criteria:

        R = SMW x MWFP / 100
        0 < SMW-R < emowse entry Mol.wt. < SMW+R

   Default search parameters are a tolerance of +/- 2 Daltons, intact Mw
   specified and the MWFP set to 25.

  emowse Scoring scheme

   The final scoring scheme is based on the frequency of a fragment
   molecular weight being found in a protein of a given range of
   molecular weight. OWL database sequence entries were initially grouped
   into 10 kDalton intact molecular weight intervals. For each 10 kDalton
   protein interval, peptide fragment molecular weights were assigned to
   cells of 100 Dalton intervals. The cells therefore contained the
   number of times a particular fragment molecular weight occurred in a
   protein of any given size. This operation was performed for each
   enzyme. Cell frequency values were calculated by dividing each cell
   value by the total number of peptides in each 10 kD protein interval.
   Cell frequency values for each 10 kDalton interval were then
   normalised to the largest cell value (Fmax), with all the cell values
   recalculated as:

        Cell value = Old value / Fmax

   to yield floating point numbers between 0 and 1. These distribution
   frequency values, calculated for each cleavage reagent, were then
   built into the emowse search program. For every database entry
   scanned, all matching fragments contribute to the final score. In the
   current implementation, non-matching fragments are ignored (neutral).
   For each matching peptide Mw a score is assigned by looking up the
   appropriate normalised distribution frequency value. In the case of
   multiple 'hits' in any one target protein (i.e. more than one matching
   peptide Mw), the distribution frequency scores are multiplied. The
   final product score is inverted and then normalised to an 'average'