## 2 Materials and Methods
### Data Representation
#### 2.2.3 Structural Contact Map Features  
The C\(\alpha\) atom coordinate information for each amino acid residue was first collected from the processed DSSP file to characterize the 3D structure of the protein. Next, the Euclidean distances between each pair of residues were calculated based on the C\(\alpha\) atom coordinates, and a protein distance map was constructed to quantify spatial relationships within the protein structure. Figure 1 illustrates the spatial relationships among C\(\alpha\) atoms in protein structures. The contact map was constructed by setting the distance threshold to 10 A; that is, if the distance between the C\(\alpha\) atoms of two residues was less than or equal to 10 A, the two residues were considered to be in contact. The critical interactions between residues in the protein structure were highlighted by setting a reasonable contactthreshold. These interactions are essential for the structural stability and functional activity of proteins. Figure 2 shows some details of the distance map and contact map obtained from the A1L4T4 protein structure data. The protein structure data and contact maps shown in Figures 1 and 2 are from _Oryza sativa_ subsp. japonica (rice).  
by performing convolutional operations on an input layer. An overview of the MMSNet approach is shown in Figure 3.  
We applied a one-dimensional (1D) convolution to the sequence data. The 1 DCNN can effectively capture the local patterns and features of long-sequence data. However, feature information at different levels and ranges is challenging to capture with a single-scale convolutional kernel, potentially leading to the omission of important long-distance dependent features and the introduction of unnecessary local feature redundancy. Therefore, a multiscale one-dimensional convolutional neural network was employed, consisting of seven groups of one-dimensional convolution and max pooling layers, with kernel sizes of 8, 16, 32, 64, 128, 256, and 512 for each group of convolution layers. Different convolutional kernels can capture the features of different dimensions. The features extracted by the multiscale convolutional kernel are combined to create a richer and more comprehensive representation of the sequence data.  
The spatial characteristics of a protein are represented in a two-dimensional image by gradually abstracting protein structure data. We used two-dimensional convolution (Conv2D) to process the image data. Two-dimensional convolution is a technique specifically designed for processing image data that effectively captures the spatial characteristics of an image by sliding a convolution kernel across a two-dimensional plane. In protein image data processing, the capture of local patterns and features of protein structures through 2D convolution supports further analyses. Pooling layers (such as max pooling and average pooling) are traditionally used by convolutional neural networks to down-sample feature maps. However, many redundant features are generated by these pooling layers, thereby increasing the computational complexity and resulting in a decrease in overall model performance. To solve this problem, we replaced the ordinary pooling layer with a residual attention mechanism. The essential features in the feature map can be adaptively weighed by the residual attention mechanism, leading to a significant reduction in the number of redundant features and enhancement in the overall performance of the model. Through a multimodal processing method, the model can make full use of the  
Figure 3: Overview of the flow of the MMSNet method. The process consists of two main parts: data processing (**a**) and model processing (**b**).  
complementary information in different modal data. The GO hierarchical classification layer is shown in Figure 4.