calculateHeavyLabels {Pbase} | R Documentation |
A function to calculate heavy labeled peptides for proteins stored
in a Proteins
object.
calculateHeavyLabels(proteins, peptides, maxN = 20L, nN = 4L, nC = 3L, endsWith = c("K", "R", "G"), ...)
proteins |
A |
peptides |
A named |
maxN |
An |
nN |
An |
nC |
An |
endsWith |
A |
... |
Additional parameters passed to |
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"))