## 4 Conclusions  
In this study, a deep learning-based multimodal learning technique was developed and applied to the prediction of grain protein functions. A significant improvement in prediction performance was shown by the MMSNet method, which integrates 1D convolutional neural networks (1 DCNN), 2D convolutional neural networks (2 DCNN), and residual attention mechanisms, and incorporates the structural information (especially secondary structure) predicted by AlphaFold2 into the model. Four-grain protein datasets for japonica, indica, maize, and wheat were preprocessed to analyze and evaluate the prediction performance of the MMSNet model. The superiority of the MMSNet model in capturing complex patterns in protein sequences and structures was emphasized by experimental comparison with existing methods, such as DeepGOCNN and DeepFRI. In particular, the extraction of relevant features was enhanced by the residual attention mechanism through a flexible and intelligent feature integration strategy. It has been experimentally proven that the model performance was significantly improved by this mechanism, which is not achievable with traditional pooling layers. This innovation highlights the potential of computer vision-related technologies in the field of protein function prediction has been highlighted by this innovation.  
Prediction of grain protein function can provide fundamental insights into food science research. For instance, predictions of enzymatic activity or molecular interactions might inspire targeted research into functional properties, such as texture enhancement or nutritional optimization, in specific food applications. This will help bridge the gap between biological understanding and practical applications in food science. The prediction function can be experimentally verified through biological methods to validate the practical applicability of our model and uncover new insights into protein function prediction. Future research should focus on exploring new methods for protein structure feature extraction, analyzing structural data from a more detailed perspective, and improving the robustness and accuracy of grain protein function prediction. In summary, new possibilities in the field of protein function prediction were identified using our multimodal, multiscale approach.  
**Author Contributions:** Conceptualization, J.L.; methodology, J.L. and X.Z.; software, X.Z.; validation, X.Z.; formal analysis, J.L., K.H. and X.G.; investigation, X.Z.; resources, J.L., K.H. and X.G.; data curation, X.Z. and Y.W.; writing--original draft preparation, X.Z.; writing--review and editing, J.L., K.H. and Y.W.; visualization, X.Z.; supervision, J.L., K.H. and X.G.; project administration, J.L.; funding acquisition, J.L. and X.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Program of Shanghai Municipal Education Commission, grant number Z-2024-312-099, under the project \"Artificial Intelligence Promoting Research Paradigm Reform and Empowering Disciplinary Advancement Program\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original data presented in the study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.13932372](https://doi.org/10.5281/zenodo.13932372)]([https://doi.org/10.5281/zenodo.13932372](https://doi.org/10.5281/zenodo.13932372)) (accessed on 15 October 2024). The analysis code for research methods is openly available on GitHub at [[https://github.com/Azxp123456/Code_Data_Proceedings.pdf](https://github.com/Azxp123456/Code_Data_Proceedings.pdf)]([https://github.com/Azxp123456/Code_Data_Proceedings.pdf](https://github.com/Azxp123456/Code_Data_Proceedings.pdf)) (accessed on 15 October 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.