  Proteins are essential for life, and their structure determines their
function. The protein secondary structure is formed by the folding of the
protein primary structure, and the protein tertiary structure is formed by the
bending and folding of the secondary structure. Therefore, the study of protein
secondary structure is very helpful to the overall understanding of protein
structure. Although the accuracy of protein secondary structure prediction has
continuously improved with the development of machine learning and deep
learning, progress in the field of protein structure prediction, unfortunately,
remains insufficient to meet the large demand for protein information.
Therefore, based on the advantages of deep learning-based methods in feature
extraction and learning ability, this paper adopts a two-dimensional fusion
deep neural network model, DstruCCN, which uses Convolutional Neural Networks
(CCN) and a supervised Transformer protein language model for single-sequence
protein structure prediction. The training features of the two are combined to
predict the protein Transformer binding site matrix, and then the
three-dimensional structure is reconstructed using energy minimization.
