Deep Learning Methods for De Novo Peptide Sequencing.

Autor: Bittremieux W; Department of Computer Science, University of Antwerp, Antwerp, Belgium., Ananth V; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA., Fondrie WE; Talus Bioscience, Seattle, Washington, USA., Melendez C; Department of Genome Sciences, University of Washington, Seattle, Washington, USA., Pominova M; Department of Computer Science, University of Antwerp, Antwerp, Belgium., Sanders J; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA., Wen B; Department of Genome Sciences, University of Washington, Seattle, Washington, USA., Yilmaz M; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA., Noble WS; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA.; Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
Jazyk: angličtina
Zdroj: Mass spectrometry reviews [Mass Spectrom Rev] 2024 Nov 29. Date of Electronic Publication: 2024 Nov 29.
DOI: 10.1002/mas.21919
Abstrakt: Protein tandem mass spectrometry data are most often interpreted by matching observed mass spectra to a protein database derived from the reference genome of the sample being analyzed. In many application domains, however, a relevant protein database is unavailable or incomplete, and in such settings de novo sequencing is required. Since the introduction of the DeepNovo algorithm in 2017, the field of de novo sequencing has been dominated by deep learning methods, which use large amounts of labeled mass spectrometry data to train multi-layer neural networks to translate from observed mass spectra to corresponding peptide sequences. Here, we describe these deep learning methods, outline procedures for evaluating their performance, and discuss the challenges in the field, both in terms of methods development and evaluation protocols.
(© 2024 Wiley Periodicals LLC.)
Databáze: MEDLINE
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