As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artificial intelligence techniques. However, these existing computational methods are still suffering from some disadvantages. In this regard, we propose a new network-based predictor called ProtFold-DFG for protein fold recognition. We propose the Directed Fusion Graph (DFG) to fuse the ranking lists generated by different methods, which employs the transitive closure to incorporate more relationships among proteins and uses the KL divergence to calculate the relationship between two proteins so as to improve its generalization ability. Finally, the PageRank algorithm is performed on the DFG to accurately recognize the protein folds by considering the global interactions among proteins in the DFG. Tested on a widely used and rigorous benchmark data set, LINDAHL dataset, experimental results show that the ProtFold-DFG outperforms the other 35 competing methods, indicating that ProtFold-DFG will be a useful method for protein fold recognition. The source code and data of ProtFold-DFG can be downloaded from http://bliulab.net/ProtFold-DFG/download

中文翻译：

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ProtFold-DFG：结合Directed Fusion Graph和PageRank算法的蛋白质折叠识别。

作为蛋白质结构预测中最重要的任务之一，蛋白质折叠识别越来越受到关注。在这方面，随着机器学习和人工智能技术的发展，已经提出了一些计算预测器。然而，这些现有的计算方法仍然存在一些缺点。在这方面，我们提出了一种新的基于网络的预测器，称为 ProtFold-DFG，用于蛋白质折叠识别。我们提出了有向融合图（DFG）来融合不同方法生成的排名列表，它采用传递闭包来合并蛋白质之间的更多关系，并使用KL散度计算两种蛋白质之间的关系，以提高其泛化能力。最后，PageRank 算法在 DFG 上执行，通过考虑 DFG 中蛋白质之间的全局相互作用来准确识别蛋白质折叠。在广泛使用且严格的基准数据集 LINDAHL 数据集上进行测试，实验结果表明 ProtFold-DFG 优于其他 35 种竞争方法，表明 ProtFold-DFG 将成为一种有用的蛋白质折叠识别方法。ProtFold-DFG的源代码和数据可以从http://bliulab.net/ProtFold-DFG/download下载