FGsub: Fusarium graminearum protein subcellular localizations predicted from primary structures.
FGsub: Fusarium graminearum protein subcellular localizations predicted from primary structures.
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DOI：
10.1186/1752-0509-4-s2-s12
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发表时间：
2010-09-13
影响因子：
--
通讯作者：
Chen L
中科院分区：
文献类型：
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作者：
Sun C;Zhao XM;Tang W;Chen L
真菌病原体禾谷镰刀菌（端型玉米赤霉菌）是多种破坏性作物疾病的病原体，其中一组基因通常协同作用导致作物病害。为了适当地发挥作用，F.一个细胞内的禾谷蛋白质应该被分配到不同的区室，即亚细胞定位。因此，F.禾谷镰刀菌蛋白质的研究有助于深入了解禾谷镰刀菌的蛋白质功能和致病机制。不幸的是，没有F的亚细胞定位信息。禾谷蛋白质现在可用。计算方法提供了一种预测F的替代方法。由于实验室生物学实验费用高、耗时长，导致禾谷镰刀菌蛋白质亚细胞定位困难。在本文中，我们开发了一种新的预测器，即FGsub，预测F。禾谷蛋白亚细胞定位的一级结构。首先，从UniProtKB数据库中收集具有亚细胞定位注释的非冗余真菌数据集并用作训练集，其中亚细胞位置被分类为10组。随后，支持向量机（SVM）的训练集上训练，并用于预测F。禾本科蛋白质亚细胞定位用于那些与训练集中的蛋白质不具有显着序列相似性的蛋白质。支持向量机在10倍交叉验证的训练集上的性能证明了该方法的有效性。此外，F.利用BLAST将同源蛋白的注释转移到未鉴定的禾谷镰刀菌中。graminearum蛋白质，使F.对禾谷蛋白质进行了更全面的注释。在这项工作中，我们提出了FGsub预测F。graminearum蛋白质亚细胞定位的综合方式。我们对该领域做出了四倍的贡献。首先，科普蛋白质亚细胞定位预测中出现的不平衡问题，提出了一种新的算法，该算法可以解决不平衡问题，避免假阳性结果。其次，我们设计了一个集成分类器，采用特征选择，以进一步提高预测精度。第三，我们使用BLAST来补充基于机器学习的方法，这扩大了我们的预测覆盖范围。最后，也是最重要的，我们预测了12786 F的亚细胞定位。禾谷镰刀菌蛋白质，提供了深入了解这种破坏性病原真菌的蛋白质功能和致病机制。
The fungal pathogen Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases, where a set of genes usually work in concert to cause diseases to crops. To function appropriately, the F. graminearum proteins inside one cell should be assigned to different compartments, i.e. subcellular localizations. Therefore, the subcellular localizations of F. graminearum proteins can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Unfortunately, there are no subcellular localization information for F. graminearum proteins available now. Computational approaches provide an alternative way to predicting F. graminearum protein subcellular localizations due to the expensive and time-consuming biological experiments in lab. In this paper, we developed a novel predictor, namely FGsub, to predict F. graminearum protein subcellular localizations from the primary structures. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB database and used as training set, where the subcellular locations are classified into 10 groups. Subsequently, Support Vector Machine (SVM) is trained on the training set and used to predict F. graminearum protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for F. graminearum proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized F. graminearum proteins so that the F. graminearum proteins are annotated more comprehensively. In this work, we present FGsub to predict F. graminearum protein subcellular localizations in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and avoid false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. Last and most important, we predict the subcellular localizations of 12786 F. graminearum proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus.