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. Testing popular stability prediction algorithms against our data shows that all perform moderately, and combinations of algorithms are better at identifying the best variants in the single mutant landscape. We find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and may be applicable toward training future stability prediction tools.

Submission Details

ID: gwoS2haU3

Submitter: Connie Wang

Submission Date: Oct. 9, 2018, 3:38 p.m.

Version: 1

Publication Details
Nisthal, A; Wang, C.Y.; Ary, M.L.; Mayo, S.L. (2018) Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis; unpublished work from Mayo Lab, Caltech group
Additional Information

Sequence Assay Result Units