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AHLF: ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides

View ORCID ProfileTom Altenburg, View ORCID ProfileSven Giese, View ORCID ProfileShengbo Wang, View ORCID ProfileThilo Muth, View ORCID ProfileBernhard Y. Renard
doi: https://doi.org/10.1101/2020.05.19.101345
Tom Altenburg
1Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering
2Digital Engineering Faculty, University of Potsdam
3Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
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Sven Giese
1Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering
2Digital Engineering Faculty, University of Potsdam
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Shengbo Wang
1Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering
2Digital Engineering Faculty, University of Potsdam
4European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
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Thilo Muth
5Federal Institute for Materials Research and Testing (BAM), Berlin, Germany
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Bernhard Y. Renard
1Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering
2Digital Engineering Faculty, University of Potsdam
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  • ORCID record for Bernhard Y. Renard
  • For correspondence: bernhard.renard@hpi.de
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Abstract

Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of a living cell on a molecular level. Currently, only a few deep learning approaches that involve peptide fragmentation spectra, which represent partial sequence information of proteins, exist. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. To elevate unrestricted learning from spectra, we introduce AHLF, a deep learning model that is end-to-end trained on 19.2 million spectra from multiple phosphoproteomic data sets. AHLF is interpretable and we show that peak-level feature importances and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared to the current-state-of-the-art on this task. To show the broad applicability of AHLF we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%. We expect our approach to directly apply to cell signaling and structural biology which use phosphoproteomic and cross-linking data, but in principal any mass spectrometry based study can benefit from an interpretable, end-to-end trained model like AHLF.

Availability https://gitlab.com/dacs-hpi/ahlf

Contact bernhard.renard{at}hpi.de

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://gitlab.com/dacs-hpi/AHLF

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 28, 2021.
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AHLF: ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides
Tom Altenburg, Sven Giese, Shengbo Wang, Thilo Muth, Bernhard Y. Renard
bioRxiv 2020.05.19.101345; doi: https://doi.org/10.1101/2020.05.19.101345
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AHLF: ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides
Tom Altenburg, Sven Giese, Shengbo Wang, Thilo Muth, Bernhard Y. Renard
bioRxiv 2020.05.19.101345; doi: https://doi.org/10.1101/2020.05.19.101345

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