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.
@article{arxiv.2407.06703,
title = {HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction},
author = {Gian Marco Visani and William Galvin and Zac Jones and Michael N. Pun and Eric Daniel and Kevin Borisiak and Utheri Wagura and Armita Nourmohammad},
journal= {arXiv preprint arXiv:2407.06703},
year = {2026}
}