Computational modeling and prediction of deletion mutants

In-frame deletion mutations can result in disease. The impact of these mutations on protein structure and subsequent functional changes remain understudied, partially due to the lack of comprehensive datasets including a structural readout. In addition, the recent breakthrough in structure prediction through deep learning demands an update of computational deletion mutation prediction. In this study, we deleted indi-vidually every residue of a small a-helical sterile alpha motif domain and investigated the structural and thermodynamic changes using 2D NMR spectroscopy and differential scanning fluorimetry. Then, we tested computational protocols to model and classify observed deletion mutants. We show a method using AlphaFold2 followed by RosettaRelax performs the best overall. In addition, a metric containing pLDDT values and Rosetta DDG is most reliable in classifying tolerated deletion mutations. We further test this method on other datasets and show they hold for proteins known to harbor disease-causing deletion mutations.

Automated summary

This study investigates the impact of in-frame deletion mutations on protein structure and function, and proposes a computational method using AlphaFold2 and RosettaRelax for prediction and classification of deletion mutants. The results demonstrate the effectiveness of this method, especially when using a metric combining pLDDT values and Rosetta DDG for classifying tolerated deletion mutations.