HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.

ABSTRACT: Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, traditional computational modeling and more recent machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutational effect and stability prediction. Pre-trained to predict amino acid propensity from its surrounding 3D structure, HERMES can be fine-tuned for mutational effects using our open-source code. We present a suite of HERMES models, pr

SUBMITTER: Visani GM 

PROVIDER: S-EPMC11261993 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature