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Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences

DNA shape is emerging as an important determinant of transcription factor binding beyond just the DNA sequence. The only tool for large scale DNA shape estimates, DNAshape was derived from Monte-Carlo simulations and predicts four broad and static DNA shape features, Propeller twist, Helical twist,...

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Autores principales: Andrabi, Munazah, Hutchins, Andrew Paul, Miranda-Saavedra, Diego, Kono, Hidetoshi, Nussinov, Ruth, Mizuguchi, Kenji, Ahmad, Shandar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5481346/
https://www.ncbi.nlm.nih.gov/pubmed/28642456
http://dx.doi.org/10.1038/s41598-017-03199-6
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author Andrabi, Munazah
Hutchins, Andrew Paul
Miranda-Saavedra, Diego
Kono, Hidetoshi
Nussinov, Ruth
Mizuguchi, Kenji
Ahmad, Shandar
author_facet Andrabi, Munazah
Hutchins, Andrew Paul
Miranda-Saavedra, Diego
Kono, Hidetoshi
Nussinov, Ruth
Mizuguchi, Kenji
Ahmad, Shandar
author_sort Andrabi, Munazah
collection PubMed
description DNA shape is emerging as an important determinant of transcription factor binding beyond just the DNA sequence. The only tool for large scale DNA shape estimates, DNAshape was derived from Monte-Carlo simulations and predicts four broad and static DNA shape features, Propeller twist, Helical twist, Minor groove width and Roll. The contributions of other shape features e.g. Shift, Slide and Opening cannot be evaluated using DNAshape. Here, we report a novel method DynaSeq, which predicts molecular dynamics-derived ensembles of a more exhaustive set of DNA shape features. We compared the DNAshape and DynaSeq predictions for the common features and applied both to predict the genome-wide binding sites of 1312 TFs available from protein interaction quantification (PIQ) data. The results indicate a good agreement between the two methods for the common shape features and point to advantages in using DynaSeq. Predictive models employing ensembles from individual conformational parameters revealed that base-pair opening - known to be important in strand separation - was the best predictor of transcription factor-binding sites (TFBS) followed by features employed by DNAshape. Of note, TFBS could be predicted not only from the features at the target motif sites, but also from those as far as 200 nucleotides away from the motif.
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spelling pubmed-54813462017-06-26 Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences Andrabi, Munazah Hutchins, Andrew Paul Miranda-Saavedra, Diego Kono, Hidetoshi Nussinov, Ruth Mizuguchi, Kenji Ahmad, Shandar Sci Rep Article DNA shape is emerging as an important determinant of transcription factor binding beyond just the DNA sequence. The only tool for large scale DNA shape estimates, DNAshape was derived from Monte-Carlo simulations and predicts four broad and static DNA shape features, Propeller twist, Helical twist, Minor groove width and Roll. The contributions of other shape features e.g. Shift, Slide and Opening cannot be evaluated using DNAshape. Here, we report a novel method DynaSeq, which predicts molecular dynamics-derived ensembles of a more exhaustive set of DNA shape features. We compared the DNAshape and DynaSeq predictions for the common features and applied both to predict the genome-wide binding sites of 1312 TFs available from protein interaction quantification (PIQ) data. The results indicate a good agreement between the two methods for the common shape features and point to advantages in using DynaSeq. Predictive models employing ensembles from individual conformational parameters revealed that base-pair opening - known to be important in strand separation - was the best predictor of transcription factor-binding sites (TFBS) followed by features employed by DNAshape. Of note, TFBS could be predicted not only from the features at the target motif sites, but also from those as far as 200 nucleotides away from the motif. Nature Publishing Group UK 2017-06-22 /pmc/articles/PMC5481346/ /pubmed/28642456 http://dx.doi.org/10.1038/s41598-017-03199-6 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Andrabi, Munazah
Hutchins, Andrew Paul
Miranda-Saavedra, Diego
Kono, Hidetoshi
Nussinov, Ruth
Mizuguchi, Kenji
Ahmad, Shandar
Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences
title Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences
title_full Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences
title_fullStr Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences
title_full_unstemmed Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences
title_short Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences
title_sort predicting conformational ensembles and genome-wide transcription factor binding sites from dna sequences
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5481346/
https://www.ncbi.nlm.nih.gov/pubmed/28642456
http://dx.doi.org/10.1038/s41598-017-03199-6
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