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A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions
Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes. Because of the expensive and time-consuming techniques required for experimental determination of complex...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624843/ https://www.ncbi.nlm.nih.gov/pubmed/34833011 http://dx.doi.org/10.3390/life11111135 |
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author | Kashiwagi, Shunya Sato, Kengo Sakakibara, Yasubumi |
author_facet | Kashiwagi, Shunya Sato, Kengo Sakakibara, Yasubumi |
author_sort | Kashiwagi, Shunya |
collection | PubMed |
description | Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes. Because of the expensive and time-consuming techniques required for experimental determination of complex protein–RNA structures, various computational methods have been developed to predict PRIs. However, most of these methods focus on predicting only RNA-binding regions in proteins or only protein-binding motifs in RNA. Methods for predicting entire residue–base contacts in PRIs have not yet achieved sufficient accuracy. Furthermore, some of these methods require the identification of 3D structures or homologous sequences, which are not available for all protein and RNA sequences. Here, we propose a prediction method for predicting residue–base contacts between proteins and RNAs using only sequence information and structural information predicted from sequences. The method can be applied to any protein–RNA pair, even when rich information such as its 3D structure, is not available. In this method, residue–base contact prediction is formalized as an integer programming problem. We predict a residue–base contact map that maximizes a scoring function based on sequence-based features such as k-mers of sequences and the predicted secondary structure. The scoring function is trained using a max-margin framework from known PRIs with 3D structures. To verify our method, we conducted several computational experiments. The results suggest that our method, which is based on only sequence information, is comparable with RNA-binding residue prediction methods based on known binding data. |
format | Online Article Text |
id | pubmed-8624843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86248432021-11-27 A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions Kashiwagi, Shunya Sato, Kengo Sakakibara, Yasubumi Life (Basel) Article Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes. Because of the expensive and time-consuming techniques required for experimental determination of complex protein–RNA structures, various computational methods have been developed to predict PRIs. However, most of these methods focus on predicting only RNA-binding regions in proteins or only protein-binding motifs in RNA. Methods for predicting entire residue–base contacts in PRIs have not yet achieved sufficient accuracy. Furthermore, some of these methods require the identification of 3D structures or homologous sequences, which are not available for all protein and RNA sequences. Here, we propose a prediction method for predicting residue–base contacts between proteins and RNAs using only sequence information and structural information predicted from sequences. The method can be applied to any protein–RNA pair, even when rich information such as its 3D structure, is not available. In this method, residue–base contact prediction is formalized as an integer programming problem. We predict a residue–base contact map that maximizes a scoring function based on sequence-based features such as k-mers of sequences and the predicted secondary structure. The scoring function is trained using a max-margin framework from known PRIs with 3D structures. To verify our method, we conducted several computational experiments. The results suggest that our method, which is based on only sequence information, is comparable with RNA-binding residue prediction methods based on known binding data. MDPI 2021-10-25 /pmc/articles/PMC8624843/ /pubmed/34833011 http://dx.doi.org/10.3390/life11111135 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kashiwagi, Shunya Sato, Kengo Sakakibara, Yasubumi A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions |
title | A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions |
title_full | A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions |
title_fullStr | A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions |
title_full_unstemmed | A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions |
title_short | A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions |
title_sort | max-margin model for predicting residue—base contacts in protein–rna interactions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624843/ https://www.ncbi.nlm.nih.gov/pubmed/34833011 http://dx.doi.org/10.3390/life11111135 |
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