calculateHeavyLabels: Calculate heavy labeled peptides
In Pbase: Manipulating and exploring protein and proteomics data

Description Usage Arguments Details Value Author(s) References Examples

View source: R/heavylabels-functions.R

A function to calculate heavy labeled peptides for proteins stored in a Proteins object.

1 2	calculateHeavyLabels(proteins, peptides, maxN = 20L, nN = 4L, nC = 3L, endsWith = c("K", "R", "G"), ...)

`proteins`	A `Proteins` object.
`peptides`	A named `character` vector containing the peptides of interest. The names must match the UniProt accession numbers of the proteins in `object`.
`maxN`	An `integer`, maximal length of the heavy labeled peptide.
`nN`	An `integer`, minimal number of amino acids at the N terminus.
`nC`	An `integer`, minimal number of amino acids at the C terminus.
`endsWith`	A `character` vector containing the allowed amino acids at the end of the resulting sequence (every peptide that doesn't end with one of these amino acids has to be one amino acid shorter as `maxN`).
`...`	Additional parameters passed to `.addOverhangs`.

The digestion efficiency with enzymes like trypsin is below 100%. That's why spiked-in peptides for labeled quantitation have to follow the same digestion rules as the peptides of interest. Therefore it is necessary to extend the peptides of interest by a few amino acids on the N- and C-terminus. These extensions should not be a cleavage point of the used enzym. This methods provides an easy interface to find the sequences for heavy labeled peptides that could be used as spike-ins for the peptides of interest. Please see the references for a more detailed discussion.

TODO: There should be a function to find the best labels for a given protein automatically.

A data.frame with 6 columns:

ProteinThe Protein accession number.
PeptideThe peptide of interest.
N_overhangThe added sequence of the N-terminus.
C_overhangThe added sequence of the C-terminus.
spikeTideResultA short description of the used creation rule.
spikeTideThe heavy labeled peptide that represents the peptide of interest best.

Sebastian Gibb <mail@sebastiangibb.de> and Pavel Shliaha

The complete description of the issue: https://github.com/sgibb/cleaver/issues/5

Kito, Keiji, et al. A synthetic protein approach toward accurate mass spectrometric quantification of component stoichiometry of multiprotein complexes. Journal of proteome research 6.2 (2007): 792-800. http://dx.doi.org/10.1021/pr060447s

## example protein database
data(p, package = "Pbase")

## digest proteins into peptides
cleavedProteins <- cleave(p)

## find spike-ins for the peptides of interest
calculateHeavyLabels(cleavedProteins,
                      peptides = c(A4UGR9 = "MEGFHIK",
                                   A4UGR9 = "QGNMYTLSK",
                                   A6H8Y1 = "GSTASNPQR"))