Entropy-driven zero-shot deep learning model selection for viral proteins

Predicting the fitness of viral proteins is fundamental to understanding viral evolution and developing antiviral strategies. This study introduces the Venus-EEM, an entropy-driven ensemble model, aimed at improving the performance of zero-shot predictions for protein fitness across diverse viral datasets. We demonstrate that entropy serves as an effective criterion for selecting optimal zero-shot models, enabling adaptive model selection for different prediction tasks. By incorporating entropy-weighted ensemble learning from multiple protein language models, Venus-EEM achieves superior performance compared to existing methods. We validate the model's effectiveness through comprehensive evaluation on multiple viral datasets and a detailed case study of T7 RNA polymerase (T7 RNAP) activity. Our findings provide an effective approach for predicting viral protein mutations based on entropy, bridging fundamental physics principles with practical biological challenges.