ArticleScientific reports2025
Severe deviation in protein fold prediction by advanced AI: a case study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies.World journal of microbiology & biotechnology · 2025Review
- Leveraging AI to explore structural contexts of post-translational modifications in drug binding.Journal of cheminformatics · 2025Article
- Leveraging AI to Explore Structural Contexts of Post-Translational Modifications in Drug Binding.bioRxiv : the preprint server for biology · 2025Article
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2 authors.
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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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