Detecting De Novo Plasmodesmata Targeting Signals and Identifying PD Targeting Proteins
Detecting De Novo Plasmodesmata Targeting Signals and Identifying PD Targeting Proteins
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检测 De Novo Plasmodesmata 靶向信号并鉴定 PD 靶向蛋白
DOI：
10.1007/978-3-030-46165-2_1
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Liao, L.
中科院分区：
文献类型：
--
作者：
Li, J.;Lee, J.-Y.;Liao, L.
蛋白质的亚细胞定位对蛋白质的功能起着重要的作用。在本文中，我们开发了一个隐马尔可夫模型来检测从头信号的蛋白质序列，目标在一个特定的细胞位置：胞间连丝。我们还开发了一个支持向量机来分类拟南芥胞间连丝定位蛋白（PDLPs），并设计了一个决策树的方法来联合收割机和HMM更好的分类性能。在对拟南芥360个I型跨膜蛋白的交叉验证中，该方法取得了良好的性能，ROC得分为0.99。在一个PDLP中预测的PD靶向信号已被实验验证。
Subcellular localization plays important roles in protein’s functioning. In this paper, we developed a hidden Markov model to detect de novo signals in protein sequences that target at a particular cellular location: plasmodesmata. We also developed a support vector machine to classify plasmodesmata located proteins (PDLPs) in Arabidopsis, and devised a decision-tree approach to combine the SVM and HMM for better classification performance. The methods achieved high performance with ROC score 0.99 in cross-validation test on a set of 360 type I transmembrane proteins in Arabidopsis. The predicted PD targeting signals in one PDLP have been experimentally verified.
影响因子：
0.6
作者：
BAUM, LE;SELL, GR
通讯作者：
SELL, GR