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Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment

Author(s):

Jianzhao Gao, Zhen Miao, Zhaopeng Zhang, Hong Wei and Lukasz Kurgan*   Pages 1 - 14 ( 14 )

Abstract:


Background: 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. Objective: 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. Results: 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. Conclusion: 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.

Keywords:

ion channel, voltage-gated ion channel, ligand-gated ion channel, prediction

Affiliation:

School of Mathematical Sciences and LPMC, Nankai University, Tianjin, College of Life Sciences, Nankai University, Tianjin, School of Mathematical Sciences and LPMC, Nankai University, Tianjin, School of Mathematical Sciences and LPMC, Nankai University, Tianjin, Department of Computer Science, Virginia Commonwealth University, Richmond



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