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bioRxiv · 10.1101/2021.12.01.470818

yHydra: Deep Learning enables an Ultra Fast Open Search by Jointly Embedding MS/MS Spectra and Peptides of Mass Spectrometry-based Proteomics

Altenburg, T.·Muth, T.·Renard, B. Y.
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Abstract

Mass spectrometry-based proteomics allows to study all proteins of a sample on a molecular level. However, mass spectra are noisy and contain complex patterns, making them inherently challenging to analyze with purely algorithmic approaches. In terms of the protein sequence landscape, most recent bottom-up MS-based proteomics studies consider either a diverse pool of post-translational modifications, employ large databases - as in metaproteomics or proteogenomics, study multiple isoforms of proteins, include unspecific cleavage sites or even combinations thereof. All this makes peptide and protein identifications challenging due to sheer size of the search space. To cope with this two-sided challenge, i.e. the complexity of real spectra and the search space size, we present a foundation model, called yHydra, that jointly embeds spectra and peptides. This allows us to implement various downstream tasks and search modes in Euclidean space. In particular, we implement an open search which allows to query multiple ten-thousands of spectra against millions of peptides. Furthermore, we implement an error-tolerant search for identifying additional proteoforms that are not included in off-the-shelf reference proteomes. Our foundation model provides meaningful embeddings, as we interpret learned peptide embeddings in comparison to the peptides physico-chemical properties. yHydras open search, assigns delta masses to each identification which allows to unrestrictedly characterize post-translational modifications. The error-tolerant mode of yHydra can be used as post-processing to existing search engines or as a standalone. yHydra is evaluated on several real life data sets for the identification of modified protein sequences and shows up to 25% increase in protein identification at constant false discovery rate compared to the current state-of-the-art. Availability(under MIT license) https://gitlab.com/dacs-hpi/yHydra Contactbernhard.renard@hpi.de

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Altenburg, T., Muth, T., Renard, B. Y.. 2021-12-03. yHydra: Deep Learning enables an Ultra Fast Open Search by Jointly Embedding MS/MS Spectra and Peptides of Mass Spectrometry-based Proteomics. https://doi.org/10.1101/2021.12.01.470818

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