Kinetic characterization of 100 glycoside hydrolase mutants enables the discovery of structural features correlated with kinetic constants

Dylan Alexander Carlin, Ryan W. Caster, Xiaokang Wang, Stephanie A. Betzenderfer, Claire X. Chen, Veasna M. Duong, Carolina V. Ryklansky, Alp Alpekin, Nathan Beaumont, Harshul Kapoor, Nicole Kim, Hosna Mohabbot, Boyu Pang, Rachel Teel, Lillian Whithaus, Ilias Tagkopoulos, Justin Siegel

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations.We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme.We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.

Original languageEnglish (US)
Article numbere0147596
JournalPLoS One
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2016

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

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    Carlin, D. A., Caster, R. W., Wang, X., Betzenderfer, S. A., Chen, C. X., Duong, V. M., Ryklansky, C. V., Alpekin, A., Beaumont, N., Kapoor, H., Kim, N., Mohabbot, H., Pang, B., Teel, R., Whithaus, L., Tagkopoulos, I., & Siegel, J. (2016). Kinetic characterization of 100 glycoside hydrolase mutants enables the discovery of structural features correlated with kinetic constants. PLoS One, 11(1), [e0147596]. https://doi.org/10.1371/journal.pone.0147596