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LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeu...

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Autores principales: Li, Sizhen, Zhang, He, Zhang, Liang, Liu, Kaibo, Liu, Boxiang, Mathews, David H., Huang, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719904/
https://www.ncbi.nlm.nih.gov/pubmed/34887342
http://dx.doi.org/10.1073/pnas.2116269118
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author Li, Sizhen
Zhang, He
Zhang, Liang
Liu, Kaibo
Liu, Boxiang
Mathews, David H.
Huang, Liang
author_facet Li, Sizhen
Zhang, He
Zhang, Liang
Liu, Kaibo
Liu, Boxiang
Mathews, David H.
Huang, Liang
author_sort Li, Sizhen
collection PubMed
description The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold’s purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5 [Formula: see text] and 3 [Formula: see text] untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.
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spelling pubmed-87199042022-01-21 LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2 Li, Sizhen Zhang, He Zhang, Liang Liu, Kaibo Liu, Boxiang Mathews, David H. Huang, Liang Proc Natl Acad Sci U S A Biological Sciences The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold’s purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5 [Formula: see text] and 3 [Formula: see text] untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics. National Academy of Sciences 2021-12-09 2021-12-28 /pmc/articles/PMC8719904/ /pubmed/34887342 http://dx.doi.org/10.1073/pnas.2116269118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Biological Sciences
Li, Sizhen
Zhang, He
Zhang, Liang
Liu, Kaibo
Liu, Boxiang
Mathews, David H.
Huang, Liang
LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2
title LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2
title_full LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2
title_fullStr LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2
title_full_unstemmed LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2
title_short LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2
title_sort linearturbofold: linear-time global prediction of conserved structures for rna homologs with applications to sars-cov-2
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719904/
https://www.ncbi.nlm.nih.gov/pubmed/34887342
http://dx.doi.org/10.1073/pnas.2116269118
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