Protein structure provides the foundation for a protein’s function. Therefore, incorporating structural information is essential in methods for protein function prediction. The attention layer in transformer models has proven to be a powerful tool for encoding rich sequential information in a wide range of machine learning tasks. Although originally developed for sequence inputs, the attention mechanism can be extended to integrate structural information. In this paper, we present a structure-aware attention mechanism that incorporates protein structural information for protein function prediction. We demonstrate that integrating structure-derived features significantly improves the performance of predicting protein functions. Our method outperforms the current state-of-the-art in comprehensive benchmark evaluations, with gains up to 29.6% in F1 score and 31.1% in area under the precision-recall curve (AUPRC). This work introduces a new approach to leveraging the transformer architecture in contexts where structural information is critical, and the framework can be further extended to integrate other types of non-sequential information.