ArticleProceedings of the National Academy of Sciences of the United States of America2023
More than just pattern recognition: Prediction of uncommon protein structure features by AI methods.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Cryo-EM Analysis in CASP16.Proteins · 2026Article
- AlphaFold 2, but not AlphaFold 3, predicts confident but unrealistic β-solenoid structures for repeat proteins.Computational and structural biotechnology journal · 2025Article
- Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold's black box.Frontiers in molecular biosciences · 2025Article
- Bridging prediction and reality: Comprehensive analysis of experimental and AlphaFold 2 full-length nuclear receptor structures.Computational and structural biotechnology journal · 2025Article
- Epigenetic Clocks: Beyond Biological Age, Using the Past to Predict the Present and Future.Aging and disease · 2024Review
- δ-Conotoxin Structure Prediction and Analysis through Large-Scale Comparative and Deep Learning Modeling Approaches.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Rosetta Energy Analysis of AlphaFold2 models: Point Mutations and Conformational Ensembles.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the AI does not have any sense of the underlying physics but is merely performing pattern recognition. Here, we address that issue by analyzing the extent to which the methods identify rare structural motifs. The rationale underlying the approach is that a pattern recognition machine tends to choose the more frequently occurring motifs, whereas some sense of subtle energetic factors is required to choose infrequently occurring ones. To reduce the possibility of bias from related experimental structures and to minimize the effect of experimental errors, we examined only CASP14 target protein crystal structures determined to a resolution limit better than 2 Å, which lacked significant amino acid sequence homology to proteins of known structure. In those experimental structures and in the corresponding models, we track
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