Efficient Interpretation of Tandem Mass Tags in Top-Down Proteomics

Abstract

Mass spectrometry is the major analytical tool for the identification and quantification of proteins in biological samples. In so-called top-down proteomics, separation and mass spectrometric analysis is performed at the level of intact proteins, without preparatory digestion steps. It has been shown that the tandem mass tag (TMT) labeling technology, which is often used for quantification based on digested proteins (bottom-up studies), can be applied in top-down proteomics as well. This, however, leads to a complex interpretation problem, where we need to annotate measured peaks with their respective generating protein, the number of charges, and the a priori unknown
number of TMT-groups attached to this protein.
In this work, we give an algorithm for the efficient enumeration of all valid
annotations that fulfill available experimental constraints. Applying the
algorithm to real-world data, we show that the annotation problem can indeed
be efficiently solved. However, our experiments also demonstrate that reliable
annotation in complex mixtures requires at least partial sequence information
and high mass accuracy and resolution to go beyond the proof-of-concept stage.