Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR
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| Publicado en: | bioRxiv (Feb 26, 2025) |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
Cold Spring Harbor Laboratory Press
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| Resumen: | We introduce AlphaFold-NMR, a novel approach to NMR structure determination that reveals previously undetected protein conformational states. Unlike conventional NMR methods that rely on NOE-derived spatial restraints, AlphaFold-NMR combines AI-driven conformational sampling with Bayesian scoring of realistic protein models against NOESY and chemical shift data. This method uncovers alternative conformational states of the enzyme Gaussia luciferase, involving large-scale changes in the lid, binding pockets, and other surface cavities. It also identifies similar yet distinct conformational states of the human tumor suppressor Cyclin-Dependent Kinase 2-Associated Protein 1. These studies demonstrate the potential of AI-based modeling with enhanced sampling to generate diverse structural models followed by conformer selection and validation with experimental data as an alternative to traditional restraint-satisfaction protocols for protein NMR structure determination. The AlphaFold-NMR framework enables discovery of conformational heterogeneity and cryptic pockets that conventional NMR analysis methods do not distinguish, providing new insights into protein structure-function relationships.Competing Interest StatementGTM is a founder of Nexomics Biosciences, Inc. This does not represent a conflict of interest for this study.Footnotes* A second successful example of hidden state discovery using the AF-NMR method has been added to the paper |
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| ISSN: | 2692-8205 |
| DOI: | 10.1101/2024.06.26.600902 |
| Fuente: | Biological Science Database |