Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis.

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

SUBMITTER: Nisthal A 

PROVIDER: S-EPMC6697890 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature