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RNA design via structure-aware multifrontier ensemble optimization
MOTIVATION: RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Addi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311297/ https://www.ncbi.nlm.nih.gov/pubmed/37387188 http://dx.doi.org/10.1093/bioinformatics/btad252 |
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author | Zhou, Tianshuo Dai, Ning Li, Sizhen Ward, Max Mathews, David H Huang, Liang |
author_facet | Zhou, Tianshuo Dai, Ning Li, Sizhen Ward, Max Mathews, David H Huang, Liang |
author_sort | Zhou, Tianshuo |
collection | PubMed |
description | MOTIVATION: RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. RESULTS: We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. AVAILABILITY AND IMPLEMENTATION: Our source code and data used in this article is available at https://github.com/shanry/SAMFEO. |
format | Online Article Text |
id | pubmed-10311297 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103112972023-07-01 RNA design via structure-aware multifrontier ensemble optimization Zhou, Tianshuo Dai, Ning Li, Sizhen Ward, Max Mathews, David H Huang, Liang Bioinformatics General Computational Biology MOTIVATION: RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. RESULTS: We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. AVAILABILITY AND IMPLEMENTATION: Our source code and data used in this article is available at https://github.com/shanry/SAMFEO. Oxford University Press 2023-06-30 /pmc/articles/PMC10311297/ /pubmed/37387188 http://dx.doi.org/10.1093/bioinformatics/btad252 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | General Computational Biology Zhou, Tianshuo Dai, Ning Li, Sizhen Ward, Max Mathews, David H Huang, Liang RNA design via structure-aware multifrontier ensemble optimization |
title | RNA design via structure-aware multifrontier ensemble optimization |
title_full | RNA design via structure-aware multifrontier ensemble optimization |
title_fullStr | RNA design via structure-aware multifrontier ensemble optimization |
title_full_unstemmed | RNA design via structure-aware multifrontier ensemble optimization |
title_short | RNA design via structure-aware multifrontier ensemble optimization |
title_sort | rna design via structure-aware multifrontier ensemble optimization |
topic | General Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311297/ https://www.ncbi.nlm.nih.gov/pubmed/37387188 http://dx.doi.org/10.1093/bioinformatics/btad252 |
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