Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey.
Department of Computer Engineering, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey.
Bioinformatics. 2019 Oct 15;35(20):4004-4010. doi: 10.1093/bioinformatics/btz238.
Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target.
In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests.
The DSPRED method can be accessed by visiting the PSP server at http://psp.agu.edu.tr. Source code and binaries are freely available at https://github.com/yusufzaferaydin/dspred.
Supplementary data are available at Bioinformatics online.
预测蛋白质的二级结构和溶剂可及性是避免更复杂的 3D 结构预测任务的基本步骤之一。利用具有已知结构的模板中包含的类别标签信息有可能提高预测方法的准确性。构建结构轮廓矩阵就是这样一种技术，它为目标的每个氨基酸位置提供了类别标签的分布。
本文提出了一种新的基于推导 PFAM 家族的结构分析技术，并将其与现有的方法相结合。在两个基准数据集和各种相似性间隔上进行的交叉验证实验表明，所提出的分析策略在统计假设检验中，与 Homolpro（一种用于整合模板信息的最先进方法）相比，表现出显著的优越性。
可以通过访问 http://psp.agu.edu.tr 上的 PSP 服务器来访问 DSPRED 方法。源代码和二进制文件可在 https://github.com/yusufzaferaydin/dspred 上免费获得。
补充数据可在 Bioinformatics 在线获得。
