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, machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutation effect prediction. Pre-trained to predict amino-acid propensities from their surrounding 3D structure atomic environments, HERMES can be efficiently fine-tuned to predict mutational effects, thanks to its symmetry-aware parameterization of the output space. Benchmarking against ot

SUBMITTER: Visani GM 

PROVIDER: S-EPMC11257601 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature