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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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. |
format | Online Article Text |
id | pubmed-8150135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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