AHLF: ad hoc learning of fragmentation patterns in mass spectra detects post-translationally modified peptides prior to database search

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. Availabilityhttps://gitlab.com/dacs-hpi/ahlf Contactbernhard.renard@hpi.de