NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation
Abstract RNA plays a crucial role not only in information transfer as messenger RNA during gene expression but also in various biological functions as non-coding RNAs. Understanding mechanical mechanisms of function needs tertiary structure information; however, experimental determination of three-d...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56261-7 |
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| author | Yuki Kagaya Zicong Zhang Nabil Ibtehaz Xiao Wang Tsukasa Nakamura Pranav Deep Punuru Daisuke Kihara |
| author_facet | Yuki Kagaya Zicong Zhang Nabil Ibtehaz Xiao Wang Tsukasa Nakamura Pranav Deep Punuru Daisuke Kihara |
| author_sort | Yuki Kagaya |
| collection | DOAJ |
| description | Abstract RNA plays a crucial role not only in information transfer as messenger RNA during gene expression but also in various biological functions as non-coding RNAs. Understanding mechanical mechanisms of function needs tertiary structure information; however, experimental determination of three-dimensional RNA structures is costly and time-consuming, leading to a substantial gap between RNA sequence and structural data. To address this challenge, we developed NuFold, a novel computational approach that leverages state-of-the-art deep learning architecture to accurately predict RNA tertiary structures. NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. NuFold incorporates a nucleobase center representation, which enables flexible conformation of ribose rings. Benchmark study showed that NuFold clearly outperformed energy-based methods and demonstrated comparable results with existing state-of-the-art deep-learning-based methods. NuFold exhibited a particular advantage in building correct local geometries of RNA. Analyses of individual components in the NuFold pipeline indicated that the performance improved by utilizing metagenome sequences for multiple sequence alignment and increasing the number of recycling. NuFold is also capable of predicting multimer complex structures of RNA by linking the input sequences. |
| format | Article |
| id | doaj-art-044abc60f6d04b01a989f0408bd8ef9d |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-044abc60f6d04b01a989f0408bd8ef9d2025-01-26T12:40:47ZengNature PortfolioNature Communications2041-17232025-01-0116111410.1038/s41467-025-56261-7NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representationYuki Kagaya0Zicong Zhang1Nabil Ibtehaz2Xiao Wang3Tsukasa Nakamura4Pranav Deep Punuru5Daisuke Kihara6Department of Biological Sciences, Purdue UniversityDepartment of Computer Science, Purdue UniversityDepartment of Computer Science, Purdue UniversityDepartment of Computer Science, Purdue UniversityDepartment of Biological Sciences, Purdue UniversityDepartment of Biological Sciences, Purdue UniversityDepartment of Biological Sciences, Purdue UniversityAbstract RNA plays a crucial role not only in information transfer as messenger RNA during gene expression but also in various biological functions as non-coding RNAs. Understanding mechanical mechanisms of function needs tertiary structure information; however, experimental determination of three-dimensional RNA structures is costly and time-consuming, leading to a substantial gap between RNA sequence and structural data. To address this challenge, we developed NuFold, a novel computational approach that leverages state-of-the-art deep learning architecture to accurately predict RNA tertiary structures. NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. NuFold incorporates a nucleobase center representation, which enables flexible conformation of ribose rings. Benchmark study showed that NuFold clearly outperformed energy-based methods and demonstrated comparable results with existing state-of-the-art deep-learning-based methods. NuFold exhibited a particular advantage in building correct local geometries of RNA. Analyses of individual components in the NuFold pipeline indicated that the performance improved by utilizing metagenome sequences for multiple sequence alignment and increasing the number of recycling. NuFold is also capable of predicting multimer complex structures of RNA by linking the input sequences.https://doi.org/10.1038/s41467-025-56261-7 |
| spellingShingle | Yuki Kagaya Zicong Zhang Nabil Ibtehaz Xiao Wang Tsukasa Nakamura Pranav Deep Punuru Daisuke Kihara NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation Nature Communications |
| title | NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation |
| title_full | NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation |
| title_fullStr | NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation |
| title_full_unstemmed | NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation |
| title_short | NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation |
| title_sort | nufold end to end approach for rna tertiary structure prediction with flexible nucleobase center representation |
| url | https://doi.org/10.1038/s41467-025-56261-7 |
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