College of Chemistry, Sichuan University, Chengdu 610064, China.
Comput Biol Chem. 2013 Dec;47:8-15. doi: 10.1016/j.compbiolchem.2013.06.002. Epub 2013 Jun 19.
Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip.
结构域是蛋白质生理功能的基础,对其进行预测是研究蛋白质结构和功能的一个有利过程。本文提出了一种新的完整自动预测方法 PPM-Dom(结构域位置预测方法),通过目标蛋白质的原子坐标来预测其结构域的特定位置。该方法集成了复杂网络、社区划分和模糊均值算子(FMO)。复杂网络和社区划分将整个序列划分为潜在的结构域区域,FMO 则允许最终确定结构域的位置。该方法足以根据查询序列的全新原子坐标信息预测将形成结构域结构的区域和非结构域区域,并能够将同一查询序列中的不同结构域彼此分离。在使用独立测试数据集评估性能时,PPM-Dom 的预测准确性达到 91.41%,敏感性达到 96.12%,特异性达到 92.86%。PPM-Dom 的工具包可在 http://cic.scu.edu.cn/bioinformatics/PPMDom.zip 上免费获取。