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Assessing the low complexity of protein sequences via the low complexity triangle

BACKGROUND: Proteins with low complexity regions (LCRs) have atypical sequence and structural features. Their amino acid composition varies from the expected, determined proteome-wise, and they do not follow the rules of structural folding that prevail in globular regions. One way to characterize th...

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Autores principales: Mier, Pablo, Andrade-Navarro, Miguel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773278/
https://www.ncbi.nlm.nih.gov/pubmed/33378336
http://dx.doi.org/10.1371/journal.pone.0239154
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author Mier, Pablo
Andrade-Navarro, Miguel A.
author_facet Mier, Pablo
Andrade-Navarro, Miguel A.
author_sort Mier, Pablo
collection PubMed
description BACKGROUND: Proteins with low complexity regions (LCRs) have atypical sequence and structural features. Their amino acid composition varies from the expected, determined proteome-wise, and they do not follow the rules of structural folding that prevail in globular regions. One way to characterize these regions is by assessing the repeatability of a sequence, that is, calculating the local propensity of a region to be part of a repeat. RESULTS: We combine two local measures of low complexity, repeatability (using the RES algorithm) and fraction of the most frequent amino acid, to evaluate different proteomes, datasets of protein regions with specific features, and individual cases of proteins with extreme compositions. We apply a representation called ‘low complexity triangle’ as a proof-of-concept to represent the low complexity measured values. Results show that proteomes have distinct signatures in the low complexity triangle, and that these signatures are associated to complexity features of the sequences. We developed a web tool called LCT (http://cbdm-01.zdv.uni-mainz.de/~munoz/lct/) to allow users to calculate the low complexity triangle of a given protein or region of interest. CONCLUSIONS: The low complexity triangle proves to be a suitable procedure to represent the general low complexity of a sequence or protein dataset. Homorepeats, direpeats, compositionally biased regions and globular regions occupy characteristic positions in the triangle. The described pipeline can be used to characterize LCRs and may help in quantifying the content of degenerated tandem repeats in proteins and proteomes.
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spelling pubmed-77732782021-01-07 Assessing the low complexity of protein sequences via the low complexity triangle Mier, Pablo Andrade-Navarro, Miguel A. PLoS One Research Article BACKGROUND: Proteins with low complexity regions (LCRs) have atypical sequence and structural features. Their amino acid composition varies from the expected, determined proteome-wise, and they do not follow the rules of structural folding that prevail in globular regions. One way to characterize these regions is by assessing the repeatability of a sequence, that is, calculating the local propensity of a region to be part of a repeat. RESULTS: We combine two local measures of low complexity, repeatability (using the RES algorithm) and fraction of the most frequent amino acid, to evaluate different proteomes, datasets of protein regions with specific features, and individual cases of proteins with extreme compositions. We apply a representation called ‘low complexity triangle’ as a proof-of-concept to represent the low complexity measured values. Results show that proteomes have distinct signatures in the low complexity triangle, and that these signatures are associated to complexity features of the sequences. We developed a web tool called LCT (http://cbdm-01.zdv.uni-mainz.de/~munoz/lct/) to allow users to calculate the low complexity triangle of a given protein or region of interest. CONCLUSIONS: The low complexity triangle proves to be a suitable procedure to represent the general low complexity of a sequence or protein dataset. Homorepeats, direpeats, compositionally biased regions and globular regions occupy characteristic positions in the triangle. The described pipeline can be used to characterize LCRs and may help in quantifying the content of degenerated tandem repeats in proteins and proteomes. Public Library of Science 2020-12-30 /pmc/articles/PMC7773278/ /pubmed/33378336 http://dx.doi.org/10.1371/journal.pone.0239154 Text en © 2020 Mier, Andrade-Navarro http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mier, Pablo
Andrade-Navarro, Miguel A.
Assessing the low complexity of protein sequences via the low complexity triangle
title Assessing the low complexity of protein sequences via the low complexity triangle
title_full Assessing the low complexity of protein sequences via the low complexity triangle
title_fullStr Assessing the low complexity of protein sequences via the low complexity triangle
title_full_unstemmed Assessing the low complexity of protein sequences via the low complexity triangle
title_short Assessing the low complexity of protein sequences via the low complexity triangle
title_sort assessing the low complexity of protein sequences via the low complexity triangle
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773278/
https://www.ncbi.nlm.nih.gov/pubmed/33378336
http://dx.doi.org/10.1371/journal.pone.0239154
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