PuSH - Publikationsserver des Helmholtz Zentrums München

Klaproth-Andrade, D.* ; Hingerl, J.* ; Bruns, Y.* ; Smith, N.H.* ; Träuble, J.* ; Wilhelm, M.* ; Gagneur, J.

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

Nat. Commun. 15:151 (2024)
Verlagsversion DOI PMC
Creative Commons Lizenzvertrag
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.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Tandem Mass-spectra; Ms/ms Spectra; Identification; Prediction
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 15, Heft: 1, Seiten: , Artikelnummer: 151 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen TUM Munich Data Science Institute (MDSI) seed fund
GPU infrastructure
Bundesministerium fr Bildung und Forschung (BMBF)
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Bundesministerium fur Bildung und Forschung (BMBF)
European Union
This work is supported by the Bundesministerium fr Bildung und Forschung (BMBF) through the project CLINSPECT-M (FKZ031L0214A)