Transfer learning to leverage larger datasets for improved prediction of protein stability changes.

ABSTRACT: Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability are important in research and medicine. Computational methods for predicting how mutations perturb protein stability are therefore of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here we introduce ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a newly released mega-scale stability dataset for training a robust stability mod

SUBMITTER: Dieckhaus H 

PROVIDER: S-EPMC10402116 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature