Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale

Abstract The local sequence context is the most fundamental feature determining the post-translational modification (PTM) of proteins. Recent technological improvements allow for the detection of new and less prevalent modifications. We found that established state-of-the-art algorithms for the dete...

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Auteurs principaux:	Theodore G. Smith, Anuli C. Uzozie, Siyuan Chen, Philipp F. Lange
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2021
Sujets:	Medicine R Science Q
Accès en ligne:	https://doaj.org/article/a88892814a84403ba85f784b07c59ce3
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spelling	oai:doaj.org-article:a88892814a84403ba85f784b07c59ce32021-11-21T12:17:42ZRobust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale10.1038/s41598-021-01971-32045-2322https://doaj.org/article/a88892814a84403ba85f784b07c59ce32021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01971-3https://doaj.org/toc/2045-2322Abstract The local sequence context is the most fundamental feature determining the post-translational modification (PTM) of proteins. Recent technological improvements allow for the detection of new and less prevalent modifications. We found that established state-of-the-art algorithms for the detection of PTM motifs in complex datasets failed to keep up with this technological development and are no longer robust. To overcome this limitation, we developed RoLiM, a new linear motif deconvolution algorithm and webserver, that enables robust and unbiased identification of local amino acid sequence determinants in complex biological systems demonstrated here by the analysis of 68 modifications found across 30 tissues in the human draft proteome map. Furthermore, RoLiM analysis of a large-scale phosphorylation dataset comprising 30 kinase inhibitors of 10 protein kinases in the EGF signalling pathway identified prospective substrate motifs for PI3K and EGFR.Theodore G. SmithAnuli C. UzozieSiyuan ChenPhilipp F. LangeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Theodore G. Smith Anuli C. Uzozie Siyuan Chen Philipp F. Lange Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale
description	Abstract The local sequence context is the most fundamental feature determining the post-translational modification (PTM) of proteins. Recent technological improvements allow for the detection of new and less prevalent modifications. We found that established state-of-the-art algorithms for the detection of PTM motifs in complex datasets failed to keep up with this technological development and are no longer robust. To overcome this limitation, we developed RoLiM, a new linear motif deconvolution algorithm and webserver, that enables robust and unbiased identification of local amino acid sequence determinants in complex biological systems demonstrated here by the analysis of 68 modifications found across 30 tissues in the human draft proteome map. Furthermore, RoLiM analysis of a large-scale phosphorylation dataset comprising 30 kinase inhibitors of 10 protein kinases in the EGF signalling pathway identified prospective substrate motifs for PI3K and EGFR.
format	article
author	Theodore G. Smith Anuli C. Uzozie Siyuan Chen Philipp F. Lange
author_facet	Theodore G. Smith Anuli C. Uzozie Siyuan Chen Philipp F. Lange
author_sort	Theodore G. Smith
title	Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale
title_short	Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale
title_full	Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale
title_fullStr	Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale
title_full_unstemmed	Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale
title_sort	robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a88892814a84403ba85f784b07c59ce3
work_keys_str_mv	AT theodoregsmith robustunsuperviseddeconvolutionoflinearmotifscharacterizes68proteinmodificationsatproteomescale AT anulicuzozie robustunsuperviseddeconvolutionoflinearmotifscharacterizes68proteinmodificationsatproteomescale AT siyuanchen robustunsuperviseddeconvolutionoflinearmotifscharacterizes68proteinmodificationsatproteomescale AT philippflange robustunsuperviseddeconvolutionoflinearmotifscharacterizes68proteinmodificationsatproteomescale
_version_	1718419086254276608

Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale

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