Journal
ACS OMEGA
Volume 5, Issue 12, Pages 6487-6493Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsomega.9b04105
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Funding
- University of California Davis
- National Institutes of Health [R01 GM 076324-11]
- National Science Foundation [1827246, 1805510, 1627539]
- National Institute of Environmental Health Sciences of the National Institutes of Health [P42ES004699]
- Direct For Biological Sciences [1827246] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr [1627539] Funding Source: National Science Foundation
- Directorate For Engineering [1805510] Funding Source: National Science Foundation
- Division Of Computer and Network Systems [1627539] Funding Source: National Science Foundation
- Div Of Biological Infrastructure [1827246] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys [1805510] Funding Source: National Science Foundation
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Engineering proteins to enhance thermal stability is a widely utilized approach for creating industrially relevant biocatalysts. The development of new experimental datasets and computational tools to guide these engineering efforts remains an active area of research. Thus, to complement the previously reported measures of T-50 and kinetic constants, we are reporting an expansion of our previously published dataset of mutants for beta-glucosidase to include both measures of T-M and Delta Delta G. For a set of 51 mutants, we found that T-50 and T-M are moderately correlated, with a Pearson correlation coefficient and Spearman's rank coefficient of 0.58 and 0.47, respectively, indicating that the two methods capture different physical features. The performance of predicted stability using nine computational tools was also evaluated on the dataset of 51 mutants, none of which are found to be strong predictors of the observed changes in T-50, T-M, or Delta Delta G. Furthermore, the ability of the nine algorithms to predict the production of isolatable soluble protein was examined, which revealed that Rosetta Delta Delta G, FoldX, DeepDDG, PoPMuSiC, and SDM were capable of predicting if a mutant could be produced and isolated as a soluble protein. These results further highlight the need for new algorithms for predicting modest, yet important, changes in thermal stability as well as a new utility for current algorithms for prescreening designs for the production of mutants that maintain fold and soluble production properties.
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