F. Potthast, B. Gerrits, J. Häkkinen,
D. Rutishauser, C.H. Ahrens, B. Roschitzki,
K. Baerenfaller, R.P. Munton, P. Walther,
P. Gehrig, P. Seif, P.H. Seeberger, and
R. Schlapbach
The mass distance fingerprint: a statistical framework for de
novo detection of predominant modifications using
high-accuracy mass spectrometry
To appear in Journal of Chromatography B
Abstract:
We describe a statistical measure, Mass Distance Fingerprint,
for automatic de novo detection of predominant peptide
mass distances, i.e., putative protein modifications. The
method's focus is to globally detect mass differences, not to
assign peptide sequences or modifications to individual
spectra. The Mass Distance Fingerprint is calculated from high
accuracy measured peptide masses. For the data sets used in this
study, known mass differences are detected at electron mass
accuracy or better. The proposed method is novel because it
works independently of protein sequence databases and without
any prior knowledge about modifications. Both modified and
unmodified peptides have to be present in the sample to be
detected. The method can be used for automated detection of
chemical/post-translational modifications, quality control of
experiments and labelling approaches, and to control the
modification settings of protein identification tools. The
algorithm is implemented as a web application and is distributed
as open source software.
LU TP 06-08