Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset

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 T50 and kinetic constants, we are reporting an expansion of our previously
published dataset of mutants for β-glucosidase to include both
measures of TM and ΔΔG. For a set of 51 mutants, we found that T50 and TM 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 T50, TM, or ΔΔG. Furthermore,
the ability of the nine algorithms to predict the production of isolatable
soluble protein was examined, which revealed that Rosetta ΔΔG, 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.