AlphaFold predictions of fold-switched conformations are driven by structure memorization
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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AlphaFold predictions of fold-switched conformations are driven by structure memorization ; volume:15 ; number:1 ; day:24 ; month:8 ; year:2024 ; pages:1-13 ; date:12.2024
Nature Communications ; 15, Heft 1 (24.8.2024), 1-13, 12.2024
- Creator
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Chakravarty, Devlina
Schafer, Joseph W.
Chen, Ethan A.
Thole, Joseph F.
Ronish, Leslie A.
Lee, Myeongsang
Porter, Lauren L.
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41467-024-51801-z
- URN
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urn:nbn:de:101:1-2411121036204.643559182116
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:33 AM CEST
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Associated
- Chakravarty, Devlina
- Schafer, Joseph W.
- Chen, Ethan A.
- Thole, Joseph F.
- Ronish, Leslie A.
- Lee, Myeongsang
- Porter, Lauren L.
- SpringerLink (Online service)
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AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination
Highly accurate protein structure prediction with AlphaFold
Improved AlphaFold modeling with implicit experimental information
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AlphaFill: enriching AlphaFold models with ligands and cofactors
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Benchmarking AlphaFold ‐enabled molecular docking predictions for antibiotic discovery
Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes
MassiveFold: unveiling AlphaFold’s hidden potential with optimized and parallelized massive sampling
DescFold: A web server for protein fold recognition
FoldHSphere: deep hyperspherical embeddings for protein fold recognition
AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination
Highly accurate protein structure prediction with AlphaFold
Improved AlphaFold modeling with implicit experimental information
Ableger : = Lessening fold
Vocal fold paralysis
AlphaFill: enriching AlphaFold models with ligands and cofactors
Inferring molecular inhibition potency with AlphaFold predicted structures
Benchmarking AlphaFold ‐enabled molecular docking predictions for antibiotic discovery
Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes
MassiveFold: unveiling AlphaFold’s hidden potential with optimized and parallelized massive sampling
DescFold: A web server for protein fold recognition
FoldHSphere: deep hyperspherical embeddings for protein fold recognition
AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination
Highly accurate protein structure prediction with AlphaFold
Improved AlphaFold modeling with implicit experimental information
Ableger : = Lessening fold
Vocal fold paralysis
AlphaFill: enriching AlphaFold models with ligands and cofactors