School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.
College of Life Sciences, Nankai University, Tianjin, China.
Curr Drug Targets. 2019;20(5):579-592. doi: 10.2174/1389450119666181022153942.
Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated.
We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors.
While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types.
Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.
离子通道是一个庞大且不断增长的蛋白质家族。其中许多与疾病有关，因此它们是 700 多种药物的靶点。计算方法可以帮助发现新的离子通道，这些方法可以从蛋白质序列预测离子通道及其类型。然而，这些方法从未被全面比较和评估过。
我们首次对基于序列的离子通道预测器进行了全面调查。我们描述了八个预测器，其中包括五种预测离子通道、它们的类型以及四类电压门控通道的方法。我们还开发并使用了一个新的基准数据集，对三种现有的预测器进行了比较性的实证分析。
虽然已经发表了几种基于不同设计的方法，但目前只有少数方法可用，并且提供了广泛的预测范围。在考虑发表新方法时，应该要求在发表后提供支持和可用性。实证分析表明，预测离子通道的性能很强，而预测离子通道类型和电压门控通道类别的性能则较为适中。我们发现当前方法的一个主要弱点是无法准确预测被归类为多个类/类型的离子通道。
现有的离子通道预测器可供终端用户使用。它们提供了实用的预测质量水平。依赖于更大、更多样化的预测输入的方法（如 PSIONplus）更准确。应该开发新的工具来解决离子通道的多标签预测问题。
