Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929550442627072 |
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| author | Chakravarty, Devlina Lee, Myeongsang Porter, Lauren L. |
| author_facet | Chakravarty, Devlina Lee, Myeongsang Porter, Lauren L. |
| contents | In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_14898 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction Chakravarty, Devlina Lee, Myeongsang Porter, Lauren L. Biomolecules In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably. |
| title | Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2410.14898 |