Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning
Abstract
Significance Enzymes find utility as therapeutics and for the production of specialty chemicals. Changing the amino acid sequence of an enzyme can increase solubility, but many such mutations disrupt catalytic activity. To evaluate this trade-off, we developed an experimental system to evaluate the relative solubility for nearly all possible single point mutants for two model enzymes. We find that the tendency for a given solubility-enhancing mutation to disrupt catalytic activity depends, among other factors, on how far the position is from the catalytic active site and whether that mutation has been sampled during evolution. We develop predictive models to identify mutations that enhance solubility without disrupting activity with an accuracy of 90%. These results have biotechnological applications.
- Authors:
-
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824,
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545,
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824,
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824,, Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1343782
- Grant/Contract Number:
- AC02-76SF00515
- Resource Type:
- Published Article
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 114 Journal Issue: 9; Journal ID: ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Klesmith, Justin R., Bacik, John-Paul, Wrenbeck, Emily E., Michalczyk, Ryszard, and Whitehead, Timothy A. Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning. United States: N. p., 2017.
Web. doi:10.1073/pnas.1614437114.
Klesmith, Justin R., Bacik, John-Paul, Wrenbeck, Emily E., Michalczyk, Ryszard, & Whitehead, Timothy A. Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning. United States. https://doi.org/10.1073/pnas.1614437114
Klesmith, Justin R., Bacik, John-Paul, Wrenbeck, Emily E., Michalczyk, Ryszard, and Whitehead, Timothy A. Tue .
"Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning". United States. https://doi.org/10.1073/pnas.1614437114.
@article{osti_1343782,
title = {Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning},
author = {Klesmith, Justin R. and Bacik, John-Paul and Wrenbeck, Emily E. and Michalczyk, Ryszard and Whitehead, Timothy A.},
abstractNote = {Significance Enzymes find utility as therapeutics and for the production of specialty chemicals. Changing the amino acid sequence of an enzyme can increase solubility, but many such mutations disrupt catalytic activity. To evaluate this trade-off, we developed an experimental system to evaluate the relative solubility for nearly all possible single point mutants for two model enzymes. We find that the tendency for a given solubility-enhancing mutation to disrupt catalytic activity depends, among other factors, on how far the position is from the catalytic active site and whether that mutation has been sampled during evolution. We develop predictive models to identify mutations that enhance solubility without disrupting activity with an accuracy of 90%. These results have biotechnological applications.},
doi = {10.1073/pnas.1614437114},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 9,
volume = 114,
place = {United States},
year = {2017},
month = {2}
}
https://doi.org/10.1073/pnas.1614437114
Web of Science
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