首页> 外文会议>2011 World Congress on Information and Communication Technologies >Classification of proteins in intracellular and secretory pathway using global descriptors of amino acid sequence
【24h】

Classification of proteins in intracellular and secretory pathway using global descriptors of amino acid sequence

机译:使用氨基酸序列的整体描述符对细胞内和分泌途径中的蛋白质进行分类

获取原文
获取原文并翻译 | 示例

摘要

It is widely recognized that the information from the amino acid sequence can serve as crucial pointers in predicting subcellular location of proteins. We introduce a new feature vector for predicting proteins targeted to various compartments in the intracellular and secretory pathway from protein sequence. Features are based on the global Composition, Transition and Distribution (CTD) of amino acid attributes such as hydrophobicity, normalized van der Waals volume, polarity, polarizability, charge, secondary structure and solvent accessibility. Sequences are considered in three equal parts and the features are extracted separately for all the three parts. Based on the feature vectors, we have trained a Support Vector Machine to classify intracellular and secretory proteins. Our method gives an accuracy of 92% in human, 88% in plant and 95% in fungi with independent dataset at root level of the protein sorting pathway.
机译:众所周知,来自氨基酸序列的信息可以作为预测蛋白质亚细胞定位的关键指标。我们引入了一种新的特征向量,用于预测从蛋白质序列到细胞内和分泌途径中各个区室的蛋白质。这些功能基于氨基酸属性的全局组成,过渡和分布(CTD),例如疏水性,范德华规范化体积,极性,极化性,电荷,二级结构和溶剂可及性。序列分为三个相等的部分,分别针对所有三个部分提取特征。基于特征向量,我们训练了一种支持向量机来对细胞内和分泌蛋白进行分类。我们的方法在蛋白质分选途径的根源具有独立的数据集,可在人类中达到92%的准确性,在植物中达到88%的准确性,在真菌中达到95%的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号