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...
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| Language: | English |
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MDPI AG
2021-10-01
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| Online Access: | https://www.mdpi.com/2075-1729/11/11/1135 |
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| author | Shunya Kashiwagi Kengo Sato Yasubumi Sakakibara |
| author_facet | Shunya Kashiwagi Kengo Sato Yasubumi Sakakibara |
| author_sort | Shunya Kashiwagi |
| collection | DOAJ |
| 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 <i>k</i>-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. |
| first_indexed | 2024-03-10T05:20:42Z |
| format | Article |
| id | doaj.art-6bfbdfd3f4b145c8a868ca278aa2370e |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-10T05:20:42Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-6bfbdfd3f4b145c8a868ca278aa2370e2023-11-23T00:03:19ZengMDPI AGLife2075-17292021-10-011111113510.3390/life11111135A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA InteractionsShunya Kashiwagi0Kengo Sato1Yasubumi Sakakibara2Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanDepartment of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanDepartment of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanProtein–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 <i>k</i>-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.https://www.mdpi.com/2075-1729/11/11/1135protein–RNA interactionRNA secondary structurestructured support vector machine |
| spellingShingle | Shunya Kashiwagi Kengo Sato Yasubumi Sakakibara A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions Life protein–RNA interaction RNA secondary structure structured support vector machine |
| 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 | protein–RNA interaction RNA secondary structure structured support vector machine |
| url | https://www.mdpi.com/2075-1729/11/11/1135 |
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