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Ion Channels

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Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels.

Computers in biology and medicine
Glycosylation is a dynamic enzymatic process that attaches glycan to proteins or other organic molecules such as lipoproteins. Research has shown that such a process in ion channel proteins plays a fundamental role in modulating ion channel functions...

Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design.

Communications biology
Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from...

Computational Ion Channel Research: from the Application of Artificial Intelligence to Molecular Dynamics Simulations.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology
Although ion channels are crucial in many physiological processes and constitute an important class of drug targets, much is still unclear about their function and possible malfunctions that lead to diseases. In recent years, computational methods ha...

Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation.

Scientific reports
Many studies have revealed changes in specific protein channels due to physiological causes such as mutation and their effects on action potential duration changes. However, no studies have been conducted to predict the type of protein channel abnorm...

Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.

Molecules (Basel, Switzerland)
Identification of drug-target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop no...

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking.

Journal of chemical information and modeling
The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening...

Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network.

Computers in biology and medicine
Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cance...

Prediction Models for Identifying Ion Channel-Modulating Peptides via Knowledge Transfer Approaches.

IEEE journal of biomedical and health informatics
Ion channels, which can be modulated by peptides, are promising drug targets for neurological, metabolic, and cardiovascular disorders. Because it is expensive and labor-intensive to experimentally screen ion channel-modulating peptides (IMPs), in-si...

NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON.

Scientific reports
One of the fundamental goals in neuroscience is to determine how the brain processes information and ultimately controls the execution of complex behaviors. Over the past four decades, there has been a steady growth in our knowledge of the morphologi...

Predicting functional effects of ion channel variants using new phenotypic machine learning methods.

PLoS computational biology
Missense variants in genes encoding ion channels are associated with a spectrum of severe diseases. Variant effects on biophysical function correlate with clinical features and can be categorized as gain- or loss-of-function. This information enables...