HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction 1. HERMES introduces a cutting-edge 3D rotationally equivariant neural network model to predict the effects of mutations on protein stability and binding, significantly improving performance compared to previous models. 2. A key feature of HERMES is its pre-training on atomic 3D structures to predict amino acid identities, which enhances accuracy when fine-tuned to predict mutation effects. 3. HERMES offers substantial improvements in speed (2.75× faster) over its predecessor H-CNN while retaining high accuracy in mutation effect predictions, making it a practical choice for large datasets. 4. The model’s architecture respects permutation anti-symmetry in mutations, ensuring that the effect of reversing a mutation is predicted as the negative of the forward mutation, a feature that most models achieve through data augmentation. 5. When tested on experimental and computational protein structures, HERMES outperformed state-of-the-art models, including Stability-Oracle and ProteinMPNN, in predicting protein stability and binding effects. 6. HERMES was rigorously benchmarked across multiple datasets, showing excellent performance even when using computationally predicted protein structures, such as those from ESMFold, for both training and testing. 7. The paper highlights HERMES' ability to predict not only stability effects but also the binding affinity changes of mutations using the SKEMPI dataset, showing high correlation with experimental data. 8. HERMES is open-source, enabling users to fine-tune the model on their specific datasets for customized mutation effect predictions, potentially accelerating discoveries in protein engineering and disease-related mutation analysis. @TheArmita @GMarcoVisani 💻Code: github.com/StatPhysBio/herme… 📜Paper: doi.org/10.1101/2024.07.09.6…