AI Insight
This study examines how single amino acid mutations affect protein structure by analyzing paired wild-type and mutant crystal structures from the Protein Data Bank. The researchers found that structural changes caused by mutations are localized near the mutation site and decay rapidly with distance. When testing AlphaFold3's ability to predict these mutation-induced changes, prediction accuracy decreased substantially as the magnitude of structural deformation increased, while a simpler physical feature (change in solvent accessibility) maintained consistent correlation across all deformation magnitudes.
Why it matters
Understanding and predicting how mutations alter protein structure is crucial for disease research and protein engineering applications. This work establishes benchmarks for computational prediction methods and reveals current limitations in predicting mutant protein structures, even as wild-type structure prediction has improved dramatically.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Proteins can possess numerous mutations relative to their wild-type amino acid sequences with minimal impact to their structure and function. However, in other cases, even a single amino acid mutation relative to the wild-type sequence can lead to a large change in structure or even a disease phenotype. While the accuracy of wild-type protein structure prediction has improved significantly in recent years, it remains difficult to accurately predict the structure of mutant proteins. Here, we characterize the local mutation-induced structural changes in proteins for a dataset of wildtype and the corresponding single-amino acid mutant x-ray crystal structures from the Protein Data Bank (PDB). We find that mutation-induced structural changes in these proteins are localized at the site of the mutation, decaying rapidly with increasing spatial distance from the mutation site. In addition, we evaluate how well AlphaFold3 can recapitulate the observed mutation-induced structural deformations in the x-ray crystal structures. We find that the accuracy of the AlphaFold3 predictions decreases strongly with increasing mutation-induced deformation. In contrast to the results for AlphaFold3, the Pearson correlation between a single physical feature, i.e. the change in solvent accessibility, and the mutation-induced deformation does not depend on the magnitude of the deformation. Our results and analyses provide a framework for further studies aimed at predicting the structural changes in proteins caused by single amino acid mutations.
Source: Towards Accurate Prediction of Mutation-Induced Changes in Protein Structure