Abstract
A major challenge in proteomics is to fully identify and characterize the post-translational modification (PTM) patterns present at any given time in cells, tissues, and organisms. Here we present a fast and reliable method ("ModifiComb") for mapping hundreds types of PTMs at a time, including novel and unexpected PTMs. The high mass accuracy of Fourier transform mass spectrometry provides in many cases unique elemental composition of the PTM through the difference DeltaM between the molecular masses of the modified and unmodified peptides, whereas the retention time difference DeltaRT between their elution in reversed-phase liquid chromatography provides an additional dimension for PTM identification. Abundant sequence information obtained with complementary fragmentation techniques using ion-neutral collisions and electron capture often locates the modification to a single residue. The (DeltaM, DeltaRT) maps are representative of the proteome and its overall modification state and may be used for database-independent organism identification, comparative proteomic studies, and biomarker discovery. Examples of newly found modifications include +12.000 Da (+C atom) incorporation into proline residues of peptides from proline-rich proteins found in human saliva. This modification is hypothesized to increase the known activity of the peptide.
Highlights
A major challenge in proteomics is to fully identify and characterize the post-translational modification (PTM) patterns present at any given time in cells, tissues, and organisms
The main MS tool in PTM detection is tandem mass spectrometry combined with a database search engine, such as Sequest [5] or Mascot [6]
To reduce the analysis time and the false positive and negative rates, a typical database search focuses upon a few types of modifications, far fewer compared with the broad variety that potentially can be present in the sample
Summary
A major challenge in proteomics is to fully identify and characterize the post-translational modification (PTM) patterns present at any given time in cells, tissues, and organisms. Examples of newly found modifications include ؉12.000 Da (؉C atom) incorporation into proline residues of peptides from proline-rich proteins found in human saliva. Allowing for many modifications in the database search increases the rate of false positives [7], eliminating which requires a much higher score threshold for identification of peptides, which leads to an enhanced number of false negative results (misses of present proteins) [7]. To reduce the analysis time and the false positive and negative rates, a typical database search focuses upon a few types of modifications, far fewer compared with the broad variety that potentially can be present in the sample.
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