Terminomics Methodologies and the Completeness of Reductive Dimethylation: A Meta-Analysis of Publicly Available Datasets
by
Mariella Hurtado Silva 1
, Iain J. Berry 1,2, Natalie Strange 1, Steven P. Djordjevic 2 and Matthew P. Padula 1,*
Mariella Hurtado Silva 1, Iain J. Berry 1,2, Natalie Strange 1, Steven P. Djordjevic 2 and Matthew P. Padula 1,*
1
Proteomics Core Facility and School of Life Sciences, Faculty of Science, University of Technology Sydney, Broadway NSW 2007, Australia
2
The ithree Institute, Faculty of Science, University of Technology Sydney, Broadway NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Proteomes 2019, 7(2), 11; https://doi.org/10.3390/proteomes7020011
Received: 16 January 2019 / Revised: 22 March 2019 / Accepted: 25 March 2019 / Published: 29 March 2019
Methods for analyzing the terminal sequences of proteins have been refined over the previous decade; however, few studies have evaluated the quality of the data that have been produced from those methodologies. While performing global N-terminal labelling on bacteria, we observed that the labelling was not complete and investigated whether this was a common occurrence. We assessed the completeness of labelling in a selection of existing, publicly available N-terminomics datasets and empirically determined that amine-based labelling chemistry does not achieve complete labelling and potentially has issues with labelling amine groups at sequence-specific residues. This finding led us to conduct a thorough review of the historical literature that showed that this is not an unexpected finding, with numerous publications reporting incomplete labelling. These findings have implications for the quantitation of N-terminal peptides and the biological interpretations of these data.
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Keywords:
terminomics; mass spectrometry; amine labelling
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Hurtado Silva, M.; Berry, I.J.; Strange, N.; Djordjevic, S.P.; Padula, M.P. Terminomics Methodologies and the Completeness of Reductive Dimethylation: A Meta-Analysis of Publicly Available Datasets. Proteomes 2019, 7, 11.
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