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


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 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.

Submission Details

ID: 3xESLyS9

Submitter: Connie Wang

Submission Date: Sept. 10, 2019, 9:59 p.m.

Version: 2

Publication Details
Nisthal A;Wang CY;Ary ML;Mayo SL,Proc Natl Acad Sci U S A (2019) Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis. PMID:31371509
Additional Information

This is an updated version of study gwoS2haU3.

Structure view and single mutant data analysis

Study data

No weblogo for data of varying length.
Colors: D E R H K S T N Q A V I L M F Y W C G P

Data Distribution

Studies with similar sequences (approximate matches)

Correlation with other assays (exact sequence matches)

Relevant UniProtKB Entries

Percent Identity Matching Chains Protein Accession Entry Name
100.0 Immunoglobulin G-binding protein G P06654 SPG1_STRSG
100.0 Immunoglobulin G-binding protein G P19909 SPG2_STRSG