Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

Predicting chromatographic retention times (RTs) has proven beneficial in proteomics but has not yet been achieved for crosslinked peptides. Here, the authors develop an RT prediction tool for crosslinked peptides and leverage predicted RTs to increase identifications in crosslinking mass spectromet...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sven H. Giese, Ludwig R. Sinn, Fritz Wegner, Juri Rappsilber
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/9ab330b45697406d85ede559b5464d05
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9ab330b45697406d85ede559b5464d05
record_format	dspace
spelling	oai:doaj.org-article:9ab330b45697406d85ede559b5464d052021-12-02T15:49:49ZRetention time prediction using neural networks increases identifications in crosslinking mass spectrometry10.1038/s41467-021-23441-02041-1723https://doaj.org/article/9ab330b45697406d85ede559b5464d052021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23441-0https://doaj.org/toc/2041-1723Predicting chromatographic retention times (RTs) has proven beneficial in proteomics but has not yet been achieved for crosslinked peptides. Here, the authors develop an RT prediction tool for crosslinked peptides and leverage predicted RTs to increase identifications in crosslinking mass spectrometry studies.Sven H. GieseLudwig R. SinnFritz WegnerJuri RappsilberNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Sven H. Giese Ludwig R. Sinn Fritz Wegner Juri Rappsilber Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
description	Predicting chromatographic retention times (RTs) has proven beneficial in proteomics but has not yet been achieved for crosslinked peptides. Here, the authors develop an RT prediction tool for crosslinked peptides and leverage predicted RTs to increase identifications in crosslinking mass spectrometry studies.
format	article
author	Sven H. Giese Ludwig R. Sinn Fritz Wegner Juri Rappsilber
author_facet	Sven H. Giese Ludwig R. Sinn Fritz Wegner Juri Rappsilber
author_sort	Sven H. Giese
title	Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
title_short	Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
title_full	Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
title_fullStr	Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
title_full_unstemmed	Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
title_sort	retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9ab330b45697406d85ede559b5464d05
work_keys_str_mv	AT svenhgiese retentiontimepredictionusingneuralnetworksincreasesidentificationsincrosslinkingmassspectrometry AT ludwigrsinn retentiontimepredictionusingneuralnetworksincreasesidentificationsincrosslinkingmassspectrometry AT fritzwegner retentiontimepredictionusingneuralnetworksincreasesidentificationsincrosslinkingmassspectrometry AT jurirappsilber retentiontimepredictionusingneuralnetworksincreasesidentificationsincrosslinkingmassspectrometry
_version_	1718385706476240896

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

Ejemplares similares