Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
Devlina Chakravarty, Myeongsang Lee, Lauren L. Porter
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 representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably. • AlphaFold-based methods often predict single protein structures with high accuracy. • However, these methods sometimes fail to predict alternative conformations. • Three explanations for these failures are discussed.