Protein Structure Prediction (PSP) is considered to be a complicated problem in computational biology. In spite of, the remarkable progress made by the co-evolution-based method in PSP, it is still a challenging and unresolved problem. Recently, along with co-evolutionary relationships, deep learning approaches have been introduced in PSP that lead to significant progress. In this paper a novel methodology using deep ResNet architecture for predicting inter-residue distance and dihedral angles is proposed, that aims to generate 125 homologous sequences in an average from a set of customized sequence database. These sequences are used to generate input features. As an outcome of neural networks, a pool of structures is generated from which the lowest potential structure is chosen as the final predicted 3-D protein structure. The proposed method is trained using 6521 protein sequences extracted from Protein Data Bank (PDB). For testing 48 protein sequences whose residue length is less than 400 residues are chosen from the 13th Critical Assessment of protein Structure Prediction (CASP 13) dataset are used. The model is compared with Alphafold, Zhang, and RaptorX. The template modeling (TM) score is used to evaluate the accuracy of the estimated structure. The proposed method produces better performances for 52% of the target sequences while that of Alphafold, Zhang, RaptorX were 10%, 22.9%, and 6% respectively. Additionally, for 37.5% target sequences, the proposed method was able to achieve accuracy greater than or equal to 0.80. The TM score obtained for the sequences under consideration were 0.69, 0.67, 0.65, and 0.58 respectively for the proposed method, Alphafold, Zhang, and RaptorX.

蛋白质结构预测（PSP）被认为是计算生物学中的一个复杂问题。尽管在 PSP 中基于协同进化的方法取得了显着进展，但它仍然是一个具有挑战性和未解决的问题。最近，随着协同进化关系，深度学习方法已被引入 PSP，并取得了重大进展。在本文中，提出了一种使用深度 ResNet 架构预测残基间距离和二面角的新方法，旨在从一组自定义序列数据库中平均生成 125 个同源序列。这些序列用于生成输入特征。作为神经网络的结果，生成结构池，从中选择最低潜在结构作为最终预测的 3-D 蛋白质结构。所提出的方法使用从蛋白质数据库 (PDB) 中提取的 6521 个蛋白质序列进行训练。为了测试残基长度小于 400 个残基的 48 个蛋白质序列，使用了第 13 次蛋白质结构预测关键评估 (CASP 13) 数据集。该模型与 Alphafold、Zhang 和 RaptorX 进行比较. 模板建模 (TM) 分数用于评估估计结构的准确性。所提出的方法对 52% 的目标序列产生了更好的性能，而 Alphafold、Zhang、RaptorX 的性能分别为 10%、22.9% 和 6%。此外，对于 37.5% 的目标序列，所提出的方法能够实现大于或等于 0.80 的准确度。对于所提出的方法、Alphafold、Zhang 和 RaptorX，所考虑的序列获得的 TM 分数分别为 0.69、0.67、0.65 和 0.58。