Predicting 22 protein localizations in budding yeast
Predicting 22 protein localizations in budding yeast
复制标题
DOI：
10.1016/j.bbrc.2004.08.113
复制
发表时间：
2004-10-15
影响因子：
3.1
通讯作者：
Chou, KC
中科院分区：
文献类型：
--
作者：
Cai, YD;Chou, KC
根据最近的实验，芽殖酵母蛋白质可明显地分为22个亚细胞位置。在这些蛋白质中，一些具有多位置特征，即，发生在不止一个地方。然而，到目前为止，所有现有的方法在预测蛋白质的亚细胞位置被开发来处理只有单位置的情况下，其中一个查询蛋白质被假定为属于一个，只有一个，亚细胞位置。为了刺激亚细胞位置预测的发展，制定了一个增强程序，这将使现有的方法来解决多位置的问题。通过折叠交叉验证测试观察到，通过增强GO-FnD-PseAA算法[BBRC 320（2004）1236]获得的成功率显著高于其他增强方法。预计增强的GO-FunD-PseAA预测器将成为一个非常有用的工具，在预测蛋白质的亚细胞定位的基础研究和实际应用。(C)2004年爱思唯尔公司All rights reserved.
According to the recent experiments, proteins in budding yeast can be distinctly classified into 22 subcellular locations. Of these proteins, some bear the multi-locational feature, i.e., occur in more than one location. However, so far all the existing methods in predicting protein subcellular location were developed to deal with only the mono-locational case where a query protein is assumed to belong to one, and only one, subcellular location. To stimulate the development of subcellular location prediction, an augmentation procedure is formulated that will enable the existing methods to tackle the multi-locational problem as well. It has been observed thru a jackknife cross-validation test that the success rate obtained by the augmented GO-FnD-PseAA algorithm [BBRC 320 (2004) 1236] is overwhelmingly higher than those by the other augmented methods. It is anticipated that the augmented GO-FunD-PseAA predictor will become a very useful tool in predicting protein subcellular localization for both basic research and practical application. (C) 2004 Elsevier Inc. All rights reserved.