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RNA inter-nucleotide 3D closeness prediction by deep residual neural networks

MOTIVATION: Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter...

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Detalles Bibliográficos
Autores principales: Sun, Saisai, Wang, Wenkai, Peng, Zhenling, Yang, Jianyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150135/
https://www.ncbi.nlm.nih.gov/pubmed/33135062
http://dx.doi.org/10.1093/bioinformatics/btaa932
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author Sun, Saisai
Wang, Wenkai
Peng, Zhenling
Yang, Jianyi
author_facet Sun, Saisai
Wang, Wenkai
Peng, Zhenling
Yang, Jianyi
author_sort Sun, Saisai
collection PubMed
description MOTIVATION: Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness. RESULTS: We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L/10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness. AVAILABILITY AND IMPLEMENTATION: The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-81501352021-05-28 RNA inter-nucleotide 3D closeness prediction by deep residual neural networks Sun, Saisai Wang, Wenkai Peng, Zhenling Yang, Jianyi Bioinformatics Original Papers MOTIVATION: Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness. RESULTS: We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L/10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness. AVAILABILITY AND IMPLEMENTATION: The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-12-10 /pmc/articles/PMC8150135/ /pubmed/33135062 http://dx.doi.org/10.1093/bioinformatics/btaa932 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
Sun, Saisai
Wang, Wenkai
Peng, Zhenling
Yang, Jianyi
RNA inter-nucleotide 3D closeness prediction by deep residual neural networks
title RNA inter-nucleotide 3D closeness prediction by deep residual neural networks
title_full RNA inter-nucleotide 3D closeness prediction by deep residual neural networks
title_fullStr RNA inter-nucleotide 3D closeness prediction by deep residual neural networks
title_full_unstemmed RNA inter-nucleotide 3D closeness prediction by deep residual neural networks
title_short RNA inter-nucleotide 3D closeness prediction by deep residual neural networks
title_sort rna inter-nucleotide 3d closeness prediction by deep residual neural networks
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150135/
https://www.ncbi.nlm.nih.gov/pubmed/33135062
http://dx.doi.org/10.1093/bioinformatics/btaa932
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