PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types
PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types
复制标题
PSIONplusm 服务器可准确预测离子通道及其类型的多标签
DOI：
10.3390/biom10060876
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发表时间：
2020-06
期刊：
影响因子：
5.5
通讯作者：
Lukasz Kurgan
中科院分区：
文献类型：
--
作者：
Jianzhao Gao;Hong Wei;Alberto Cano;Lukasz Kurgan
离子通道的计算预测有助于从蛋白质序列中识别可能的离子通道。在过去的十年里，发展了几个离子通道及其类型的预测因子。虽然它们提供了相当准确的预测，但它们也存在一些缺点，包括缺乏可用性、并行预测模式、单标签预测(无法预测多个通道子类型)和不完全范围(无法预测电压门控通道的子类型)。我们开发了一种首创的PSIONplusm方法，它对电压门控和配体门控的离子通道及其亚型进行顺序多标记预测。PSIONplusm从PSIONplus预测器中顺序组合了三个基于支持向量机的模型产生的输出，并可作为Web服务器使用。实证检验表明，PSIONplusm在离子通道亚型的多标记预测方面优于目前的方法。这包括用户可用的现有单标记方法、结合多个单标记方法产生的结果的幼稚多标记预测器以及基于序列比对和结构域注释进行预测的方法。我们还发现，目前的方法(包括PSIONplusm)无法准确预测几个最不频繁出现的离子通道亚型。因此，当大量的注释离子通道可用于训练预测模型时，应该开发新的预测器。
Computational prediction of ion channels facilitates the identification of putative ion channels from protein sequences. Several predictors of ion channels and their types were developed in the last quindecennial. While they offer reasonably accurate predictions, they also suffer a few shortcomings including lack of availability, parallel prediction mode, single-label prediction (inability to predict multiple channel subtypes), and incomplete scope (inability to predict subtypes of the voltage-gated channels). We developed a first-of-its-kind PSIONplusm method that performs sequential multi-label prediction of ion channels and their subtypes for both voltage-gated and ligand-gated channels. PSIONplusm sequentially combines the outputs produced by three support vector machine-based models from the PSIONplus predictor and is available as a webserver. Empirical tests show that PSIONplusm outperforms current methods for the multi-label prediction of the ion channel subtypes. This includes the existing single-label methods that are available to the users, a naïve multi-label predictor that combines results produced by multiple single-label methods, and methods that make predictions based on sequence alignment and domain annotations. We also found that the current methods (including PSIONplusm) fail to accurately predict a few of the least frequently occurring ion channel subtypes. Thus, new predictors should be developed when a larger quantity of annotated ion channels will be available to train predictive models.
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影响因子：
2.9
作者：
Ying Huang;Beifang Niu;Ying Gao;L. Fu;Weizhong Li
通讯作者：
Ying Huang;Beifang Niu;Ying Gao;L. Fu;Weizhong Li
影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
--
作者：
Zhao H;Yang Y;Zhou Y
通讯作者：
Zhou Y
DOI：
10.1093/bioinformatics/bts565
发表时间：
2012-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Fu L;Niu B;Zhu Z;Wu S;Li W
通讯作者：
Li W
影响因子：
3.1
作者：
A. Tiwari;R. Srivastava
通讯作者：
A. Tiwari;R. Srivastava