DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations

Thermostability is an important property of proteins and a critical factor for their wide application. Accurate prediction of [Formula: see text] enables the estimation of the impact of mutations on thermostability in advance. A range of [Formula: see text] prediction methods based on machine learning has now emerged. However, their prediction performance remains limited due to insufficiently informative training features and little effort has been made to integrate feature calculation resources. Based on this, we integrated 12 computational resources to develop a pipeline capable of automatically calculating 1,547 features. In addition, a feature-enriched DDGWizard dataset was created, including 15,752 [Formula: see text] data. Furthermore, we performed feature selection and developed an accurate [Formula: see text] prediction model that achieved an R2 of 0.61 in cross-validation. It also outperformed several other representative prediction methods in comparisons with independent datasets. Together, the feature calculation pipeline, DDGWizard dataset, and prediction model constitute the DDGWizard system, freely available for [Formula: see text] analysis and prediction.