Abstract
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
individually every residue of a small $\alpha$-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 $\Delta$$\Delta$G 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.
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