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-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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
Channels (Austin, Tex.) - 18(2024), 1 vom: 26. März, Seite 2325032 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Nguyen, Phuong Tran [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 07.03.2024 Date Revised 25.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1080/19336950.2024.2325032 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369358244 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Review | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a AlphaFold | |
| 650 | 4 | |a ESMFold | |
| 650 | 4 | |a RoseTTAFold | |
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| 650 | 4 | |a voltage-gated calcium channels | |
| 650 | 4 | |a voltage-gated potassium chnanels | |
| 650 | 4 | |a voltage-gated sodium channels | |
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| 700 | 1 | |a Harris, Brandon John |e verfasserin |4 aut | |
| 700 | 1 | |a Mateos, Diego Lopez |e verfasserin |4 aut | |
| 700 | 1 | |a González, Adriana Hernández |e verfasserin |4 aut | |
| 700 | 1 | |a Murray, Adam Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Yarov-Yarovoy, Vladimir |e verfasserin |4 aut | |
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