Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

The identification of HLA peptides by mass spectrometry is non-trivial. Here, the authors extended and used the wealth of data from the ProteomeTools project to improve the prediction of non-tryptic peptides using deep learning, and show their approach enables a variety of immunological discoveries.

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Autores principales:	Mathias Wilhelm, Daniel P. Zolg, Michael Graber, Siegfried Gessulat, Tobias Schmidt, Karsten Schnatbaum, Celina Schwencke-Westphal, Philipp Seifert, Niklas de Andrade Krätzig, Johannes Zerweck, Tobias Knaute, Eva Bräunlein, Patroklos Samaras, Ludwig Lautenbacher, Susan Klaeger, Holger Wenschuh, Roland Rad, Bernard Delanghe, Andreas Huhmer, Steven A. Carr, Karl R. Clauser, Angela M. Krackhardt, Ulf Reimer, Bernhard Kuster
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1610571c49f445ee8754bc881b81762b
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spelling	oai:doaj.org-article:1610571c49f445ee8754bc881b81762b2021-12-02T17:34:34ZDeep learning boosts sensitivity of mass spectrometry-based immunopeptidomics10.1038/s41467-021-23713-92041-1723https://doaj.org/article/1610571c49f445ee8754bc881b81762b2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23713-9https://doaj.org/toc/2041-1723The identification of HLA peptides by mass spectrometry is non-trivial. Here, the authors extended and used the wealth of data from the ProteomeTools project to improve the prediction of non-tryptic peptides using deep learning, and show their approach enables a variety of immunological discoveries.Mathias WilhelmDaniel P. ZolgMichael GraberSiegfried GessulatTobias SchmidtKarsten SchnatbaumCelina Schwencke-WestphalPhilipp SeifertNiklas de Andrade KrätzigJohannes ZerweckTobias KnauteEva BräunleinPatroklos SamarasLudwig LautenbacherSusan KlaegerHolger WenschuhRoland RadBernard DelangheAndreas HuhmerSteven A. CarrKarl R. ClauserAngela M. KrackhardtUlf ReimerBernhard KusterNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Mathias Wilhelm Daniel P. Zolg Michael Graber Siegfried Gessulat Tobias Schmidt Karsten Schnatbaum Celina Schwencke-Westphal Philipp Seifert Niklas de Andrade Krätzig Johannes Zerweck Tobias Knaute Eva Bräunlein Patroklos Samaras Ludwig Lautenbacher Susan Klaeger Holger Wenschuh Roland Rad Bernard Delanghe Andreas Huhmer Steven A. Carr Karl R. Clauser Angela M. Krackhardt Ulf Reimer Bernhard Kuster Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
description	The identification of HLA peptides by mass spectrometry is non-trivial. Here, the authors extended and used the wealth of data from the ProteomeTools project to improve the prediction of non-tryptic peptides using deep learning, and show their approach enables a variety of immunological discoveries.
format	article
author	Mathias Wilhelm Daniel P. Zolg Michael Graber Siegfried Gessulat Tobias Schmidt Karsten Schnatbaum Celina Schwencke-Westphal Philipp Seifert Niklas de Andrade Krätzig Johannes Zerweck Tobias Knaute Eva Bräunlein Patroklos Samaras Ludwig Lautenbacher Susan Klaeger Holger Wenschuh Roland Rad Bernard Delanghe Andreas Huhmer Steven A. Carr Karl R. Clauser Angela M. Krackhardt Ulf Reimer Bernhard Kuster
author_facet	Mathias Wilhelm Daniel P. Zolg Michael Graber Siegfried Gessulat Tobias Schmidt Karsten Schnatbaum Celina Schwencke-Westphal Philipp Seifert Niklas de Andrade Krätzig Johannes Zerweck Tobias Knaute Eva Bräunlein Patroklos Samaras Ludwig Lautenbacher Susan Klaeger Holger Wenschuh Roland Rad Bernard Delanghe Andreas Huhmer Steven A. Carr Karl R. Clauser Angela M. Krackhardt Ulf Reimer Bernhard Kuster
author_sort	Mathias Wilhelm
title	Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
title_short	Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
title_full	Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
title_fullStr	Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
title_full_unstemmed	Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
title_sort	deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1610571c49f445ee8754bc881b81762b
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Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

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