Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold
Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-ba...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Channels |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19336950.2024.2325032 |
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| _version_ | 1846136270327119872 |
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| author | Phuong Tran Nguyen Brandon John Harris Diego Lopez Mateos Adriana Hernández González Adam Michael Murray Vladimir Yarov-Yarovoy |
| author_facet | Phuong Tran Nguyen Brandon John Harris Diego Lopez Mateos Adriana Hernández González Adam Michael Murray Vladimir Yarov-Yarovoy |
| author_sort | Phuong Tran Nguyen |
| collection | DOAJ |
| description | Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel – CaV1.1, and human voltage-gated potassium (KV) channel – KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research. |
| format | Article |
| id | doaj-art-beab5ea70fd84c6f9e25b028bb4eafa2 |
| institution | Kabale University |
| issn | 1933-6950 1933-6969 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Channels |
| spelling | doaj-art-beab5ea70fd84c6f9e25b028bb4eafa22024-12-09T07:27:27ZengTaylor & Francis GroupChannels1933-69501933-69692024-12-0118110.1080/19336950.2024.2325032Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFoldPhuong Tran Nguyen0Brandon John Harris1Diego Lopez Mateos2Adriana Hernández González3Adam Michael Murray4Vladimir Yarov-Yarovoy5Department of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USADepartment of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USADepartment of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USADepartment of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USAMonterey Peninsula College, Monterey, CA, USADepartment of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USAIon channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel – CaV1.1, and human voltage-gated potassium (KV) channel – KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.https://www.tandfonline.com/doi/10.1080/19336950.2024.2325032Structural modelingvoltage-gated sodium channelsvoltage-gated calcium channelsvoltage-gated potassium chnanelsAlphaFoldRoseTTAFold |
| spellingShingle | Phuong Tran Nguyen Brandon John Harris Diego Lopez Mateos Adriana Hernández González Adam Michael Murray Vladimir Yarov-Yarovoy Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold Channels Structural modeling voltage-gated sodium channels voltage-gated calcium channels voltage-gated potassium chnanels AlphaFold RoseTTAFold |
| title | Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold |
| title_full | Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold |
| title_fullStr | Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold |
| title_full_unstemmed | Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold |
| title_short | Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold |
| title_sort | structural modeling of ion channels using alphafold2 rosettafold2 and esmfold |
| topic | Structural modeling voltage-gated sodium channels voltage-gated calcium channels voltage-gated potassium chnanels AlphaFold RoseTTAFold |
| url | https://www.tandfonline.com/doi/10.1080/19336950.2024.2325032 |
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