Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
National Institutes of Health · National Center for Biotechnology Information · +1 more institution
Abstract
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…
Citation impact
- FWCI
- 36.97
- Percentile
- 100%
- References
- 54
Authors
3- DCDevlina Chakravarty
National Institutes of Health, National Center for Biotechnology Information
- MLMyeongsang Lee
National Institutes of Health, National Center for Biotechnology Information
- LLLauren L. PorterCorresponding
National Center for Biotechnology Information, National Heart Lung and Blood Institute
Topics & keywords
- Pairwise comparison
- Training set
- Set (abstract data type)
- Protein structure prediction
- Protein structure
- Artificial intelligence
- Computational biology
- Computer science