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.

翻译：蛋白质是生命活动的基础，其结构决定功能。蛋白质一级结构通过折叠形成二级结构，二级结构进一步弯曲折叠形成三级结构。因此，对蛋白质二级结构的研究有助于全面理解蛋白质结构。尽管随着机器学习和深度学习的发展，蛋白质二级结构预测的准确性持续提升，但蛋白质结构预测领域的进展仍难以满足对蛋白质信息的巨大需求。基于深度学习方法在特征提取与学习能力上的优势，本文提出了一种二维融合深度神经网络模型DstruCCN，该模型结合卷积神经网络（CNN）与监督型Transformer蛋白质语言模型，实现单序列蛋白质结构预测。通过融合两者的训练特征预测蛋白质Transformer结合位点矩阵，并利用能量最小化方法重建三维结构。