Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models.

ABSTRACT: Machine learning-based protein mutational effect prediction is widely used in protein engineering and pathogenicity prediction, but training data scarcity remains a major challenge due to high costs of experimental measurements. A previous study proposed data augmentation using computational estimates by molecular simulation. However, this approach has been limited to predicting mutational effects on thermostability. Here, we present a new data augmentation method that combines molecular simulation with zero-shot prediction computed by protein language models. These computational estimates serve as 'weak' training data to supplement experimental training data. Our method dynamically adjusts the weight and inclusion of weak training data based on available experimental training data. This r

SUBMITTER: Deguchi T 

PROVIDER: S-EPMC12513171 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature