Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.

ABSTRACT: Insight into how mutations affect protein stability is crucial for protein engineering, understanding genetic diseases, and exploring protein evolution. Numerous computational methods have been developed to predict the impact of amino acid substitutions on protein stability. Nevertheless, comparing these methods poses challenges due to variations in their training data. Moreover, it is observed that they tend to perform better at predicting destabilizing mutations than stabilizing ones. Here, we meticulously compiled a new dataset from three recently published databases: ThermoMutDB, FireProtDB, and ProThermDB. This dataset, which does not overlap with the well-established S2648 dataset, consists of 4038 single-point mutations, including over 1000 stabilizing mutations. We assessed these m

SUBMITTER: Zheng F 

PROVIDER: S-EPMC10751734 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature