Flagging False Positives Following Untargeted LC–MS Characterization of Histone Post-Translational Modification Combinations

Epigenetic changes
can be studied with an untargeted characterization
of histone post-translational modifications (PTMs) by liquid chromatography–mass
spectrometry (LC–MS).
While prior information about more than 20 types of histone PTMs exists,
little is known about histone PTM combinations (PTMCs). Because of
the combinatorial explosion it is intrinsically impossible to consider
all potential PTMCs in a database search. Consequentially, high-scoring
false positives with unconsidered but correct alternative isobaric
PTMCs can occur. Current quality controls can neither estimate the
amount of unconsidered alternatives nor flag potential false positives.
Here, we propose a conceptual workflow that provides such options.
In this workflow, an in silico modeling of all candidate isoforms
with known-to-exist PTMs is made. The most frequently occurring PTM
sets of these candidate isoforms are determined and used in several
database searches. This suppresses the combinatorial explosion while
considering as many candidate isoforms as possible. Finally, annotations
can be classified as unique or ambiguous, the latter implying false
positives. This workflow was evaluated on an LC–MS data set
containing 44 histone extracts. We were able to consider 60% of all
candidate isoforms. Importantly, 40% of all annotations were classified
as ambiguous. This highlights the need for a more thorough evaluation
of modified peptide annotations.