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Prediction of virus-host protein-protein interactions mediated by short linear motifs

BACKGROUND: Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filter...

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Autores principales: Becerra, Andrés, Bucheli, Victor A., Moreno, Pedro A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345135/
https://www.ncbi.nlm.nih.gov/pubmed/28279163
http://dx.doi.org/10.1186/s12859-017-1570-7
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author Becerra, Andrés
Bucheli, Victor A.
Moreno, Pedro A.
author_facet Becerra, Andrés
Bucheli, Victor A.
Moreno, Pedro A.
author_sort Becerra, Andrés
collection PubMed
description BACKGROUND: Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). RESULTS: We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them – with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. CONCLUSION: We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1570-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-53451352017-03-14 Prediction of virus-host protein-protein interactions mediated by short linear motifs Becerra, Andrés Bucheli, Victor A. Moreno, Pedro A. BMC Bioinformatics Research Article BACKGROUND: Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). RESULTS: We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them – with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. CONCLUSION: We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1570-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-09 /pmc/articles/PMC5345135/ /pubmed/28279163 http://dx.doi.org/10.1186/s12859-017-1570-7 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Becerra, Andrés
Bucheli, Victor A.
Moreno, Pedro A.
Prediction of virus-host protein-protein interactions mediated by short linear motifs
title Prediction of virus-host protein-protein interactions mediated by short linear motifs
title_full Prediction of virus-host protein-protein interactions mediated by short linear motifs
title_fullStr Prediction of virus-host protein-protein interactions mediated by short linear motifs
title_full_unstemmed Prediction of virus-host protein-protein interactions mediated by short linear motifs
title_short Prediction of virus-host protein-protein interactions mediated by short linear motifs
title_sort prediction of virus-host protein-protein interactions mediated by short linear motifs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345135/
https://www.ncbi.nlm.nih.gov/pubmed/28279163
http://dx.doi.org/10.1186/s12859-017-1570-7
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