Understanding Protein Function with a Multimodal Retrieval-Augmented Foundation Model 1. PoET-2, a new protein language model, achieves state-of-the-art performance in predicting the effects of mutations on protein function, especially for challenging cases like insertions/deletions and higher-order mutations. This model combines sequence, structure, and evolutionary information in a novel way to improve protein understanding and design capabilities. 2. The model incorporates a hierarchical transformer encoder and dual decoders with both causal and masked language modeling objectives. This dual training approach allows PoET-2 to excel in both generative tasks (like sequence generation) and bidirectional representation learning, making it versatile for various protein-related tasks. 3. PoET-2 leverages retrieval augmentation, which enables it to learn from context and incorporate new sequences not present in the original training data. This feature enhances its ability to adapt to different protein families and their specific evolutionary constraints, leading to more accurate predictions. 4. In zero-shot variant effect prediction, PoET-2 outperforms previous models significantly, especially on datasets involving multiple mutations and indels. It also shows superior performance in supervised settings with limited data, demonstrating excellent data efficiency and generalization ability. 5. The model's architecture includes a structure-based attention bias mechanism, which integrates structural information into the attention operations. This enhances the model's ability to capture 3D structural relationships, contributing to its improved performance in tasks related to protein structure and function. 6. PoET-2 is compact, with only 182 million parameters, making it efficient and scalable. Despite its smaller size, it matches or exceeds the performance of much larger models, highlighting its efficiency and practicality for real-world applications in protein engineering and design. 7. The authors demonstrate PoET-2's effectiveness across various benchmarks, including deep mutational scanning and clinical datasets. The model's ability to predict the fitness effects of mutations accurately can accelerate the development of new therapeutics and enhance our understanding of disease mechanisms. 📜Paper: arxiv.org/abs/2508.04724 #ProteinEngineering #AIinBiology #MachineLearning #ProteinFunction #MutationPrediction