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

TL;DR

HERMES is a novel 3D equivariant neural network that predicts protein mutational effects and stability, outperforming existing models by leveraging structure-based pre-training and fine-tuning.

Contribution

Introduction of HERMES, a structure-based neural network with 3D rotational equivariance, pre-trained for amino acid propensity prediction and fine-tuned for mutational effect prediction.

Findings

HERMES often outperforms existing models in mutational effect prediction.

HERMES can be fine-tuned for specific predictive tasks.

HERMES demonstrates versatility across stability, binding, and fitness predictions.

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, pre-trained with different strategies, and fine-tuned to predict the stability effect of mutations. Benchmarking against other models shows that HERMES often outperforms or matches their performance in predicting mutational effect on stability, binding, and fitness. HERMES offers versatile tools for evaluating mutational effects and can be fine-tuned for specific predictive objectives.