Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

Klaproth-Andrade, D.; Hingerl, J.; Smith, N. H.; Trauble, J.; Wilhelm, M.; Gagneur, J.

doi:10.1101/2023.01.05.522752

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bioRxiv · 10.1101/2023.01.05.522752

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

Klaproth-Andrade, D.·Hingerl, J.·Smith, N. H.·Trauble, J.·Wilhelm, M.·Gagneur, J.

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Abstract

Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a new de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a new convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a new peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.

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Klaproth-Andrade, D., Hingerl, J., Smith, N. H., Trauble, J., Wilhelm, M., Gagneur, J.. 2023-01-06. Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing. https://doi.org/10.1101/2023.01.05.522752

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