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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Nature Portfolio
2021
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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) |
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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 |
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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 |