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

Daniela Klaproth-Andrade, Johannes Hingerl, Yanik Bruns, Nicholas H. Smith, Jakob Träuble, Mathias Wilhelm () and Julien Gagneur ()
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Daniela Klaproth-Andrade: Technical University of Munich
Johannes Hingerl: Technical University of Munich
Yanik Bruns: Technical University of Munich
Nicholas H. Smith: Technical University of Munich
Jakob Träuble: Technical University of Munich
Mathias Wilhelm: Technical University of Munich
Julien Gagneur: Technical University of Munich

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: 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 de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a 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 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.

Date: 2024
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DOI: 10.1038/s41467-023-44323-7

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