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Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions

Ana Sílvia Ferreira Diamantino Coelho e Silva, Robbin Bouwmeester (UGent) , Lennart Martens (UGent) and Sven Degroeve (UGent)
(2019) BIOINFORMATICS. 35(24). p.5243-5248
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Abstract
Motivation: The use of post-processing tools to maximize the information gained from a proteomics search engine is widely accepted and used by the community, with the most notable example being Percolator-a semi-supervised machine learning model which learns a new scoring function for a given dataset. The usage of such tools is however bound to the search engine's scoring scheme, which doesn't always make full use of the intensity information present in a spectrum. We aim to show how this tool can be applied in such a way that maximizes the use of spectrum intensity information by leveraging another machine learning-based tool, MS2PIP. MS2PIP predicts fragment ion peak intensities. Results: We show how comparing predicted intensities to annotated experimental spectra by calculating direct similarity metrics provides enough information for a tool such as Percolator to accurately separate two classes of peptide-to-spectrum matches. This approach allows using more information out of the data (compared with simpler intensity based metrics, like peak counting or explained intensities summing) while maintaining control of statistics such as the false discovery rate.
Keywords
PROTEIN IDENTIFICATION, MS/MS

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Citation

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MLA
Ferreira Diamantino Coelho e Silva, Ana Sílvia, et al. “Accurate Peptide Fragmentation Predictions Allow Data Driven Approaches to Replace and Improve upon Proteomics Search Engine Scoring Functions.” BIOINFORMATICS, vol. 35, no. 24, 2019, pp. 5243–48, doi:10.1093/bioinformatics/btz383.
APA
Ferreira Diamantino Coelho e Silva, A. S., Bouwmeester, R., Martens, L., & Degroeve, S. (2019). Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions. BIOINFORMATICS, 35(24), 5243–5248. https://doi.org/10.1093/bioinformatics/btz383
Chicago author-date
Ferreira Diamantino Coelho e Silva, Ana Sílvia, Robbin Bouwmeester, Lennart Martens, and Sven Degroeve. 2019. “Accurate Peptide Fragmentation Predictions Allow Data Driven Approaches to Replace and Improve upon Proteomics Search Engine Scoring Functions.” BIOINFORMATICS 35 (24): 5243–48. https://doi.org/10.1093/bioinformatics/btz383.
Chicago author-date (all authors)
Ferreira Diamantino Coelho e Silva, Ana Sílvia, Robbin Bouwmeester, Lennart Martens, and Sven Degroeve. 2019. “Accurate Peptide Fragmentation Predictions Allow Data Driven Approaches to Replace and Improve upon Proteomics Search Engine Scoring Functions.” BIOINFORMATICS 35 (24): 5243–5248. doi:10.1093/bioinformatics/btz383.
Vancouver
1.
Ferreira Diamantino Coelho e Silva AS, Bouwmeester R, Martens L, Degroeve S. Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions. BIOINFORMATICS. 2019;35(24):5243–8.
IEEE
[1]
A. S. Ferreira Diamantino Coelho e Silva, R. Bouwmeester, L. Martens, and S. Degroeve, “Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions,” BIOINFORMATICS, vol. 35, no. 24, pp. 5243–5248, 2019.
@article{8620729,
  abstract     = {{Motivation: The use of post-processing tools to maximize the information gained from a proteomics search engine is widely accepted and used by the community, with the most notable example being Percolator-a semi-supervised machine learning model which learns a new scoring function for a given dataset. The usage of such tools is however bound to the search engine's scoring scheme, which doesn't always make full use of the intensity information present in a spectrum. We aim to show how this tool can be applied in such a way that maximizes the use of spectrum intensity information by leveraging another machine learning-based tool, MS2PIP. MS2PIP predicts fragment ion peak intensities.

Results: We show how comparing predicted intensities to annotated experimental spectra by calculating direct similarity metrics provides enough information for a tool such as Percolator to accurately separate two classes of peptide-to-spectrum matches. This approach allows using more information out of the data (compared with simpler intensity based metrics, like peak counting or explained intensities summing) while maintaining control of statistics such as the false discovery rate.}},
  author       = {{Ferreira Diamantino Coelho e Silva, Ana Sílvia and Bouwmeester, Robbin and Martens, Lennart and Degroeve, Sven}},
  issn         = {{1367-4803}},
  journal      = {{BIOINFORMATICS}},
  keywords     = {{PROTEIN IDENTIFICATION,MS/MS}},
  language     = {{eng}},
  number       = {{24}},
  pages        = {{5243--5248}},
  title        = {{Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions}},
  url          = {{http://doi.org/10.1093/bioinformatics/btz383}},
  volume       = {{35}},
  year         = {{2019}},
}
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