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 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 two prediction algorithms, and that including more algorithms can provide diminishing returns. In most cases, poor in silico predictions were tied to compositional differences between the data being analyzed and the datasets used to train the algorithm. Finally, we find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and that data produced by these methods may be applicable toward training future stability prediction tools. Significance StatementUsing liquid-handling automation, we constructed and measured the thermodynamic stability of almost every single mutant of protein G (G{beta}1), a small domain. This self-consistent dataset is the largest of its kind and offers unique opportunities on two fronts: (i) insight into protein domain properties such as positional sensitivity and incorporated amino acid tolerance, and (ii) service as a validation set for future efforts in protein stability prediction. As G{beta}1 is a model system for protein folding and design, and its single mutant landscape has been measured by deep mutational scanning, we expect our dataset to serve as a reference for studies aimed at extracting stability information from fitness data or developing novel high-throughput stability assays.