CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction
Protein structure prediction is a challenge. A new deep learning framework, CopulaNet, is a major step forward toward end-to-end prediction of inter-residue distances and protein tertiary structures with improved accuracy and efficiency.
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-22869-8 |
| _version_ | 1818342689703198720 |
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| author | Fusong Ju Jianwei Zhu Bin Shao Lupeng Kong Tie-Yan Liu Wei-Mou Zheng Dongbo Bu |
| author_facet | Fusong Ju Jianwei Zhu Bin Shao Lupeng Kong Tie-Yan Liu Wei-Mou Zheng Dongbo Bu |
| author_sort | Fusong Ju |
| collection | DOAJ |
| description | Protein structure prediction is a challenge. A new deep learning framework, CopulaNet, is a major step forward toward end-to-end prediction of inter-residue distances and protein tertiary structures with improved accuracy and efficiency. |
| first_indexed | 2024-12-13T16:18:41Z |
| format | Article |
| id | doaj.art-33cc2239e1a44129b8b0dddfeb060858 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-13T16:18:41Z |
| publishDate | 2021-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-33cc2239e1a44129b8b0dddfeb0608582022-12-21T23:38:46ZengNature PortfolioNature Communications2041-17232021-05-011211910.1038/s41467-021-22869-8CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure predictionFusong Ju0Jianwei Zhu1Bin Shao2Lupeng Kong3Tie-Yan Liu4Wei-Mou Zheng5Dongbo Bu6Key Lab of Intelligent Information Processing, State Key Lab of Computer Architecture, Big-data Academy, Institute of Computing Technology, Chinese Academy of SciencesMicrosoft Research AsiaMicrosoft Research AsiaKey Lab of Intelligent Information Processing, State Key Lab of Computer Architecture, Big-data Academy, Institute of Computing Technology, Chinese Academy of SciencesMicrosoft Research AsiaUniversity of Chinese Academy of SciencesKey Lab of Intelligent Information Processing, State Key Lab of Computer Architecture, Big-data Academy, Institute of Computing Technology, Chinese Academy of SciencesProtein structure prediction is a challenge. A new deep learning framework, CopulaNet, is a major step forward toward end-to-end prediction of inter-residue distances and protein tertiary structures with improved accuracy and efficiency.https://doi.org/10.1038/s41467-021-22869-8 |
| spellingShingle | Fusong Ju Jianwei Zhu Bin Shao Lupeng Kong Tie-Yan Liu Wei-Mou Zheng Dongbo Bu CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction Nature Communications |
| title | CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction |
| title_full | CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction |
| title_fullStr | CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction |
| title_full_unstemmed | CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction |
| title_short | CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction |
| title_sort | copulanet learning residue co evolution directly from multiple sequence alignment for protein structure prediction |
| url | https://doi.org/10.1038/s41467-021-22869-8 |
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