Severe deviation in protein fold prediction by advanced AI: a case study
Abstract Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino acid sequences. In spite of these remarkable advances,...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-89516-w |
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| Summary: | Abstract Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino acid sequences. In spite of these remarkable advances, experimental structure determination remains critical. Here we report severe deviations between the experimental structure of a two-domain protein and its equivalent AI-prediction. These observations are particularly relevant to the relative orientation of the domains within the global protein scaffold. We observe positional divergence in equivalent residues beyond 30 Å, and an overall RMSD of 7.7 Å. Significant deviation between experimental structures and AI-predicted models echoes the presence of unusual conformations, insufficient training data and high complexity in protein folding that can ultimately lead to current limitations in protein structure prediction. |
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| ISSN: | 2045-2322 |