Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativa L.)
Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativa L.)
复制标题
水稻异源三聚体 G 蛋白效应子的预测和鉴定 (Oryza sativa L.)
DOI：
10.1093/bib/bbw021
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发表时间：
2017-03-01
影响因子：
9.5
通讯作者：
He，Huaqin
中科院分区：
文献类型：
--
作者：
Li，Kuan;Xu，Chaoqun;He，Huaqin
异源三聚体G蛋白信号级联是后生动物将细胞与环境联系起来的主要传感机制之一。然而，在植物中实验鉴定的G蛋白效应子的数量有限。因此，我们研究了哪些工具最适合预测水稻中的G蛋白效应子。在这里，我们比较了四个分类器的预测性能与8种不同的编码方案的G蛋白的效应器，使用10倍交叉验证。四种方法进行了评估：随机森林，朴素贝叶斯，K-近邻和支持向量机。我们将这些方法应用于实验鉴定G蛋白的效应子和随机选择的非效应子蛋白，并测试其灵敏度和特异性。结果表明，采用K间隔氨基酸对组合和基序或结构域组合的随机森林分类器（CKSAAP_PROSITE_200）的分类效果最好，准确率和Mathew相关系数分别达到74.62%和0.49。我们开发了一个在线预测器G-Effector，它在预测G蛋白效应子方面优于BLAST，PSI-BLAST和HMMER。这为研究人员选择结合不同特征选择编码方案的分类器提供了有价值的指导。利用G-Effector筛选水稻G蛋白的效应子，并通过基因共表达数据确定候选效应子。有趣的是，在前15名候选人中，有一个没有出现在训练数据集中，但在以前的研究工作中得到了验证。因此，本文中的候选效应物列表为研究人员提供了一条线索，以了解它们的功能，并为未来的实验工作提供了验证框架。可在http://bioinformatics.fafu.edu.cn/geffector上查阅。
Heterotrimeric G protein signaling cascades are one of the primary metazoan sensing mechanisms linking a cell to environment. However, the number of experimentally identified effectors of G protein in plant is limited. We have therefore studied which tools are best suited for predicting G protein effectors in rice. Here, we compared the predicting performance of four classifiers with eight different encoding schemes on the effectors of G proteins by using 10-fold cross-validation. Four methods were evaluated: random forest, naive Bayes, K-nearest neighbors and support vector machine. We applied these methods to experimentally identified effectors of G proteins and randomly selected non-effector proteins, and tested their sensitivity and specificity. The result showed that random forest classifier with composition of K-spaced amino acid pairs and composition of motif or domain (CKSAAP_PROSITE_200) combination method yielded the best performance, with accuracy and the Mathew's correlation coefficient reaching 74.62% and 0.49, respectively. We have developed G-Effector, an online predictor, which outperforms BLAST, PSI-BLAST and HMMER on predicting the effectors of G proteins. This provided valuable guidance for the researchers to select classifiers combined with different feature selection encoding schemes. We used G-Effector to screen the effectors of G protein in rice, and confirmed the candidate effectors by gene co-expression data. Interestingly, one of the top 15 candidates, which did not appear in the training data set, was validated in a previous research work. Therefore, the candidate effectors list in this article provides both a clue for researchers as to their function and a framework of validation for future experimental work. It is accessible at http://bioinformatics.fafu.edu.cn/geffector.