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

LiGIoNs: A computational method for the detection and classification of ligand-gated ion channels

Ligand-Gated Ion Channels (LGICs) is one of the largest groups of transmembrane proteins. Due to their major role in synaptic transmission, both in the nervous system and the somatic neuromuscular junction, LGICs present attractive therapeutic targets. During the last few years, several computational methods for the detection of LGICs have been developed. These methods are based on machine learning approaches utilizing features extracted solely from the amino acid composition. Here we report the development of LiGIoNs, a profile Hidden Markov Model (pHMM) method for the prediction and ligand-based classification of LGICs. The method consists of a library of 10 pHMMs, one per LGIC subfamily, built from the alignment of representative LGIC sequences. In addition, 14 Pfam pHMMs are used to further annotate and classify unknown protein sequences into one of the 10 LGIC subfamilies. Evaluation of the method showed that it outperforms existing methods in the detection of LGICs. On top of that, LiGIoNs is the only currently available method that classifies LGICs into subfamilies. The method is available online at http://bioinformatics.biol.uoa.gr/ligions/.

Endogenous ion channels expressed in human embryonic kidney (HEK-293) cells

Mammalian expression systems, particularly the human embryonic kidney (HEK-293) cells, combined with electrophysiological studies, have greatly benefited our understanding of the function, characteristic, and regulation of various ion channels. It was previously assumed that the existence of endogenous ion channels in native HEK-293 cells could be negligible. Still, more and more ion channels are gradually reported in native HEK-293 cells, which should draw our attention. In this regard, we summarize the different ion channels that are endogenously expressed in HEK-293 cells, including voltage-gated Na⁺ channels, Ca²⁺ channels, K⁺ channels, Cl^- channels, nonselective cation channels, TRP channels, acid-sensitive ion channels, and Piezo channels, which may complicate the recording of the heterogeneously expressed ion channels to a certain degree. We noted that the expression patterns and channel profiles varied with different studies, which may be due to the distinct originality of the cells, cell culture conditions, passage numbers, and different recording protocols. Therefore, a better knowledge of endogenous ion channels may help minimize potential problems in characterizing heterologously expressed ion channels. Based on this, it is recommended that HEK-293 cells from unknown sources should be examined before transfection for the characterization of their functional profile, especially when the expression level of exogenous ion channels does not overwhelm the endogenous ion channels largely, or the current amplitude is not significantly higher than the native currents.

Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method

MOTIVATION

The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in the protein surface or inside of protein. However, this computation will fail when the residues information is missing.

RESULTS

In this article, we proposed a novel method for estimation RSA using the Cα atom distance matrix with the deep learning method (EAGERER). The new method, EAGERER, achieves Pearson correlation coefficients of 0.921-0.928 on two independent test datasets. We empirically demonstrate that EAGERER can yield better Pearson correlation coefficients than existing RSA estimators, such as coordination number, half sphere exposure and SphereCon. To the best of our knowledge, EAGERER represents the first method to estimate the solvent accessible area using limited information with a deep learning model. It could be useful to the protein structure and protein function prediction.

AVAILABILITYAND IMPLEMENTATION

The method is free available at https://github.com/cliffgao/EAGERER.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The function of SUMOylation and its crucial roles in the development of neurological diseases

Neurological diseases are relatively complex diseases of a large system; however, the detailed mechanism of their pathogenesis has not been completely elucidated, and effective treatment methods are still lacking for some of the diseases. The SUMO (small ubiquitin-like modifier) modification is a dynamic and reversible process that is catalyzed by SUMO-specific E1, E2, and E3 ligases and reversed by a family of SENPs (SUMO/Sentrin-specific proteases). SUMOylation covalently conjugates numerous cellular proteins, and affects their cellular localization and biological activity in numerous cellular processes. A wide range of neuronal proteins have been identified as SUMO substrates, and the disruption of SUMOylation results in defects in synaptic plasticity, neuronal excitability, and neuronal stress responses. SUMOylation disorders cause many neurodegenerative diseases, such as Parkinson's disease, Alzheimer's disease, and Huntington's disease. By modulating the ion channel subunit, SUMOylation imbalance is responsible for the development of various channelopathies. The regulation of protein SUMOylation in neurons may provide a new strategy for the development of targeted therapeutic drugs for neurodegenerative diseases and channelopathies.

PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types

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 PSIONplus^m method that performs sequential multi-label prediction of ion channels and their subtypes for both voltage-gated and ligand-gated channels. PSIONplus^m 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 PSIONplus^m 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 PSIONplus^m) 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.