Open AccessThermal stability and kinetic constants for 129 variants of a family 1 glycoside hydrolase reveal that enzyme activity and stability can be separately designed
Author(s) -
Dylan Alexander Carlin,
Siena Hapig-Ward,
Bill Wayne Chan,
Natalie Damrau,
Mary Riley,
Ryan Caster,
Bowen Bethards,
Justin B. Siegel
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0176255
Subject(s) - enzyme kinetics , stability (learning theory) , thermal stability , hydrolase , michaelis–menten kinetics , glycoside hydrolase , kinetics , directed evolution , chemistry , enzyme , protein engineering , thermodynamics , biological system , computational chemistry , mutant , biology , biochemistry , physics , enzyme assay , computer science , active site , organic chemistry , quantum mechanics , machine learning , gene
Accurate modeling of enzyme activity and stability is an important goal of the protein engineering community. However, studies seeking to evaluate current progress are limited by small data sets of quantitative kinetic constants and thermal stability measurements. Here, we report quantitative measurements of soluble protein expression in E . coli , thermal stability, and Michaelis-Menten constants ( k cat , K M , and k cat /K M ) for 129 designed mutants of a glycoside hydrolase. Statistical analyses reveal that functional T m is independent of k cat , K M , and k cat /K M in this system, illustrating that an individual mutation can modulate these functional parameters independently. In addition, this data set is used to evaluate computational predictions of protein stability using the established Rosetta and FoldX algorithms. Predictions for both are found to correlate only weakly with experimental measurements, suggesting improvements are needed in the underlying algorithms.