Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis

Proceedings of the National Academy of Sciences of the United States of America
Alex NisthalStephen L. Mayo

Abstract

The accurate prediction of protein stability upon sequence mutation is an important but unsolved challenge in protein engineering. Large mutational datasets are required to train computational predictors, but traditional methods for collecting stability data are either low-throughput or measure protein stability indirectly. Here, we develop an automated method to generate thermodynamic stability data for nearly every single mutant in a small 56-residue protein. Analysis reveals that most single mutants have a neutral effect on stability, mutational sensitivity is largely governed by residue burial, and unexpectedly, hydrophobics are the best tolerated amino acid type. Correlating the output of various stability-prediction algorithms against our data shows that nearly all perform better on boundary and surface positions than for those in the core and are better at predicting large-to-small mutations than small-to-large ones. We show that the most stable variants in the single-mutant landscape are better identified using combinations of 2 prediction algorithms and including more algorithms can provide diminishing returns. In most cases, poor in silico predictions were tied to compositional differences between the data being analy...Continue Reading

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Citations

Apr 15, 2020·Nature Communications·Juannan Zhou, David M McCandlish
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Nov 11, 2021·Biophysical Journal·Matt SternkeDoug Barrick

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