Deep learning the collisional cross sections of the peptide universe from a million experimental values

Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS predic...

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Autores principales:	Florian Meier, Niklas D. Köhler, Andreas-David Brunner, Jean-Marc H. Wanka, Eugenia Voytik, Maximilian T. Strauss, Fabian J. Theis, Matthias Mann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f630ea4ee7ac476f8a6cf713b4000619
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id	oai:doaj.org-article:f630ea4ee7ac476f8a6cf713b4000619
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spelling	oai:doaj.org-article:f630ea4ee7ac476f8a6cf713b40006192021-12-02T12:11:16ZDeep learning the collisional cross sections of the peptide universe from a million experimental values10.1038/s41467-021-21352-82041-1723https://doaj.org/article/f630ea4ee7ac476f8a6cf713b40006192021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21352-8https://doaj.org/toc/2041-1723Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS prediction.Florian MeierNiklas D. KöhlerAndreas-David BrunnerJean-Marc H. WankaEugenia VoytikMaximilian T. StraussFabian J. TheisMatthias MannNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Florian Meier Niklas D. Köhler Andreas-David Brunner Jean-Marc H. Wanka Eugenia Voytik Maximilian T. Strauss Fabian J. Theis Matthias Mann Deep learning the collisional cross sections of the peptide universe from a million experimental values
description	Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS prediction.
format	article
author	Florian Meier Niklas D. Köhler Andreas-David Brunner Jean-Marc H. Wanka Eugenia Voytik Maximilian T. Strauss Fabian J. Theis Matthias Mann
author_facet	Florian Meier Niklas D. Köhler Andreas-David Brunner Jean-Marc H. Wanka Eugenia Voytik Maximilian T. Strauss Fabian J. Theis Matthias Mann
author_sort	Florian Meier
title	Deep learning the collisional cross sections of the peptide universe from a million experimental values
title_short	Deep learning the collisional cross sections of the peptide universe from a million experimental values
title_full	Deep learning the collisional cross sections of the peptide universe from a million experimental values
title_fullStr	Deep learning the collisional cross sections of the peptide universe from a million experimental values
title_full_unstemmed	Deep learning the collisional cross sections of the peptide universe from a million experimental values
title_sort	deep learning the collisional cross sections of the peptide universe from a million experimental values
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f630ea4ee7ac476f8a6cf713b4000619
work_keys_str_mv	AT florianmeier deeplearningthecollisionalcrosssectionsofthepeptideuniversefromamillionexperimentalvalues AT niklasdkohler deeplearningthecollisionalcrosssectionsofthepeptideuniversefromamillionexperimentalvalues AT andreasdavidbrunner deeplearningthecollisionalcrosssectionsofthepeptideuniversefromamillionexperimentalvalues AT jeanmarchwanka deeplearningthecollisionalcrosssectionsofthepeptideuniversefromamillionexperimentalvalues AT eugeniavoytik deeplearningthecollisionalcrosssectionsofthepeptideuniversefromamillionexperimentalvalues AT maximiliantstrauss deeplearningthecollisionalcrosssectionsofthepeptideuniversefromamillionexperimentalvalues AT fabianjtheis deeplearningthecollisionalcrosssectionsofthepeptideuniversefromamillionexperimentalvalues AT matthiasmann deeplearningthecollisionalcrosssectionsofthepeptideuniversefromamillionexperimentalvalues
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Deep learning the collisional cross sections of the peptide universe from a million experimental values

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