Abstract
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.
Highlights
Ion channels are integral membrane proteins that regulate the flow of anions and voltage potential across cellular membranes
The ion channels were collected from UniProt [14] by using the gene ontology (GO) [24,25] molecular function annotations
We ensured that these ion channels and non-ion channels share low sequence similarity,
Summary
Ion channels are integral membrane proteins that regulate the flow of anions and voltage potential across cellular membranes. The other three predictors, which include the method by Tiwari and Srivastava [21], the method by Han et al [22], and PSIONplus [23], address the prediction of ion channels, their types (voltage- vs ligand-gated) and the four subtypes of the voltage-gated channels (potassium, sodium, calcium, and anions). The current tools predict a single outcome for each input protein sequence, while some of the channels may transport multiple types of ions This requires a multi-label prediction where a given method can output multiple ion channel subtypes. PSIONplusm builds on the top of arguably the most accurate current method [13], PSIONplus [23] It performs the prediction in a sequential manner, makes multi-label predictions that allow to identify channels that transport multiple ion types, and covers the subtypes for both the voltage-gated and the ligand-gated channels. The corresponding standalone code can be obtained from https://github.com/cliffgao/PSIONplusm
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