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

ABSTRACT: 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 β-glucosidase to include both measures of T _M and ΔΔG. 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

SUBMITTER: Huang P 

PROVIDER: S-EPMC7114132 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature