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Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis

Nisthal, Alex and Wang, Connie Y. and Ary, Marie L. and Mayo, Stephen L. (2019) Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis. Proceedings of the National Academy of Sciences of the United States of America, 116 (33). pp. 16367-16377. ISSN 0027-8424. PMCID PMC6697890.

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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.

Item Type:Article
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URLURL TypeDescription Information CentralArticle Paper
Wang, Connie Y.0000-0003-2971-3971
Ary, Marie L.0000-0002-0756-1746
Mayo, Stephen L.0000-0002-9785-5018
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, (ID no. gwoS2haU3). This article contains supporting information online at
Funding AgencyGrant Number
National Security Science and Engineering Faculty FellowshipUNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Subject Keywords:thermodynamic stability; mutagenesis; protein engineering; protein stability prediction; protein G
Issue or Number:33
PubMed Central ID:PMC6697890
Record Number:CaltechAUTHORS:20181207-083716880
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Official Citation:Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis. Alex Nisthal, Connie Y. Wang, Marie L. Ary, Stephen L. Mayo. Proceedings of the National Academy of Sciences Aug 2019, 116 (33) 16367-16377; DOI: 10.1073/pnas.1903888116
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91558
Deposited By: George Porter
Deposited On:07 Dec 2018 16:57
Last Modified:03 Apr 2020 23:17

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