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Protein Interface Residues Recognition Using Granular Computing Theory

机译：基于颗粒计算理论的蛋白质界面残基识别

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摘要

Predicting of protein-protein interaction sites (PPIs) merely are researched in a single granular space in which the correlations among different levels are neglected. In this paper, PPIs models are constructed in different granular spaces based on Quotient Space Theory. We mainly use HSSP profile and PSI-Blast profile as two features for granular space, then we use granularity synthesis theory to synthesis PPIs models from different features, finally we also improve the prediction by the number of neighboring residue. With the above method, an accuracy of 59.99% with sensitivity (68.87%), CC (0.2113), F-measure (53.12%) and specificity (47.56%) is achieved after considering different level results. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Best success is then achieved with sensitivity, specificity, CC, accuracy and F-measure pegged at 74.96%, 47.87%, 0.2458, 59.63% and 54.66%, respectively. Experimental results presented here demonstrate that multi-granular method can be applied to automated identification of protein interface residues.
机译：蛋白质-蛋白质相互作用位点（PPI）的预测仅在单个颗粒空间中进行，其中忽略了不同水平之间的相关性。本文基于商空间理论，在不同的粒度空间中构造了PPI模型。我们主要使用HSSP轮廓和PSI-Blast轮廓作为粒度空间的两个特征，然后使用粒度综合理论从不同特征合成PPI模型，最后还通过相邻残基的数量来改进预测。通过上述方法，在考虑不同水平的结果后，灵敏度（68.87％），CC（0.2113），F量度（53.12％）和特异性（47.56％）的准确度达到59.99％。然后，我们开发一种后处理方案，以使用预测残基的相对位置来改善预测。然后，灵敏度，特异性，CC，准确性和F度量分别为74.96％，47.87％，0.2458、59.63％和54.66％，可获得最大的成功。本文介绍的实验结果表明，多颗粒方法可应用于蛋白质界面残基的自动鉴定。

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  • 来源
    《Rough sets and knowledge technology》|2010年|p.727-734|共8页
  • 会议地点 Beijing(CN);Beijing(CN)
  • 作者

    Jiaxing Cheng; Xiuquan Du; Jiehua Cheng;

  • 作者单位

    Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui, China;

    Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui, China;

    Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui, China,Department of Computer Science, Anhui Science and Technology University, Fengyang, Anhui, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 程序设计、软件工程;
  • 关键词

    protein-protein interaction; SVM; granular computing; sequence profile;

    机译：蛋白质相互作用支持向量机;粒度计算；序列图;

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