Predicting how mutations affect protein stability and protein-protein binding affinity is crucial for protein engineering and drug development. Although several computational tools have been developed for these tasks, they often require specialized expertise and are difficult to integrate into unified workflows. Here, we present PythiaStudio (https://pythiastudio.wulab.xyz), a comprehensive web platform that integrates our recently developed Pythia, Pythia-PPI, and Pythia-Pocket models with complementary protein analysis tools. The platform enables users to predict mutational effects on protein stability and protein-protein binding affinity, ligand binding pocket, through an intuitive interface. Additional features include fitness and structure prediction. PythiaStudio provides interactive visualization tools, including mutation heatmaps, sortable result tables, and structure viewers. Importantly, the platform offers an integrated engineering workflow that combines stability and fitness predictions to guide rational protein design. We demonstrate the utility of this workflow through multiple cases, including different glycoside hydrolases and amidases. In these cases, the two-step computational redesign strategy successfully improved both thermostability and catalytic activity. PythiaStudio democratizes access to state-of-the-art deep learning-based protein engineering methods, enabling researchers without computational expertise to perform sophisticated protein engineering.
Sun et al. (Mon,) studied this question.