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Published August 13, 2019 | Published + Supplemental Material
Journal Article Open

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.

Additional Information

© 2019 National Academy of Sciences. Published under the PNAS license. Contributed by Stephen L. Mayo, July 3, 2019 (sent for review March 6, 2019; reviewed by Elizabeth M. Meiering and Timothy Whitehead). A.N. thanks Jost Vielmetter for advice and feedback on the automated platform. S.L.M. acknowledges grants from the National Security Science and Engineering Faculty Fellowship program and the Defense Advanced Research Projects Agency Protein Design Processes program. Author contributions: A.N. and S.L.M. designed research; A.N. performed research; A.N. and C.Y.W. contributed new reagents/analytic tools; A.N., C.Y.W., and M.L.A. analyzed data; and A.N., C.Y.W., M.L.A., and S.L.M. wrote the paper. Reviewers: E.M.M., University of Waterloo; and T.W., University of Colorado Boulder. The authors declare no conflict of interest. Data deposition: The data reported in this paper have been deposited in Protabank, https://www.protabank.org/ (ID no. gwoS2haU3). This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1903888116/-/DCSupplemental.

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Published - 16367.full.pdf

Supplemental Material - pnas.1903888116.sapp.pdf


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