Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset.

TitleEvaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset.
Publication TypeJournal Article
Year of Publication2020
AuthorsHuang P, Chu SKS, Frizzo HN, Connolly MP, Caster RW, Siegel JB
JournalACS Omega
Date Published2020 Mar 31

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 and kinetic constants, we are reporting an expansion of our previously published dataset of mutants for β-glucosidase to include both measures of and ΔΔ. For a set of 51 mutants, we found that and 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 , , or ΔΔ. Furthermore, the ability of the nine algorithms to predict the production of isolatable soluble protein was examined, which revealed that Rosetta ΔΔ, 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.

Alternate JournalACS Omega
PubMed ID32258884
PubMed Central IDPMC7114132