## 1 Introduction  
With the growing global population and increasing demand for healthy diets, food science and technology are facing unprecedented challenges and opportunities [1]. As an indispensable part of the human diet, grains are not only a source of primary energy and nutrition but are also utilized as essential raw materials in the food industry owing to their rich protein content [2]. The unique functional properties of grain proteins [3], such as solubility, emulsification, and gelling ability, influence the texture, taste, and nutritional value of foods. This study focuses on predicting the biological functions of grain proteins based on gene ontology (GO) terms, and the study of functional properties is beyond the scope of this study. Gene ontology describes the molecular function, biological process, or cellular component roles of proteins. The gene ontology function prediction in this study used protein amino acid sequence data and structural data. Functional properties (such as solubility, emulsification, and gelling ability) are determined by the molecular structure and chemical properties of proteins [4]. Gene ontology function prediction can provide indirect reference data for studying functional properties. Therefore, it is necessary to understand and predict the function of grain proteins. Traditional protein function prediction relies on time-consuming and labor-intensive biological experiments. However, with the development of high-throughput sequencing technology and bioinformatics, computational methods are now being used to effectively accelerate the annotation of protein function. Earlier methods, such as BLAST [5], transferred protein functional annotations based on sequence similarity. With the development of artificial intelligence, many machine-learning methods have been widely used to predict protein function with good results [6; 7; 8; 9]. However, redundancy and noise problems resulting from the accumulation of large amounts of protein data have become bottlenecks that limit the performance improvement of machine-learning algorithms. [PERSON] first used a convolutional neural network (CNN) to construct a deep-learning model, DeepGO [10], and combined protein sequences and protein-protein interaction network features to predict protein functions. In the later proposed DeepGOPlus [11], Diamond (a fast sequence alignment tool) was used to obtain sequence similarity information [12], and a CNN was combined to extract sequence features to improve prediction performance. Protein function is not only determined by its primary structure (sequence) but is also closely related to its specific spatial structure. The folding and spatial conformation of proteins are decisive factors in determining their functions [13; 14]. DeepFRI uses a graph convolutional network (GCN) to link sequence and structural data and achieves better performance in protein function prediction by using experimentally determined protein structure data [15]. Deep learning methods can accurately capture subtle patterns closely related to functions in massive amounts of protein data owing to their powerful pattern recognition and automatic feature extraction capabilities [16]. Previous research on protein function prediction was mostly limited to protein sequence data or relied on the acquisition of expensive experimental structural data. However, a new perspective on protein function prediction was provided through a breakthrough in protein structure prediction achieved using AlphaFold2 [17]. The accuracy of protein structure prediction has been improved to the atomic level using AlphaFold2, providing high-quality structural predictions close to the experimental levels. The availability of such high-quality structural data has greatly enriched the resources for computational approaches. TransFun extracts features from protein sequences using a pre-trained protein language model (evolutionary scale modeling, ESM) via transfer learning and combines them with protein 3D structures through equivariant graph neural networks [18].