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Bioinformatics. 2019 Dec 15;35(24):5243-5248. doi: 10.1093/bioinformatics/btz383.

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

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VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.
Department of Biomolecular Medicine, Faculty of Medicine, Ghent, Belgium.
Bioinformatics Institute, Ghent University, Ghent, Belgium.



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.


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.


All of the code is available online at


Supplementary data are available at Bioinformatics online.

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