scholarly journals iCDI-W2vCom: Identifying the Ion Channel–Drug Interaction in Cellular Networking Based on word2vec and node2vec

2021 ◽  
Vol 12 ◽  
Author(s):  
Jie Zheng ◽  
Xuan Xiao ◽  
Wang-Ren Qiu
Keyword(s):  
Drug Interaction ◽  
Ion Channels ◽  
Ion Channel ◽  
Drug Target ◽  
Research Work ◽  
Potential Method ◽  
3D Structures ◽  
Target Drug ◽  
Drug Compound ◽  
User Friendly

Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimer’s disease, epilepsy, cephalagra, and type II diabetes. In the research work for predicting ion channel–drug, computational approaches are effective and efficient compared with the costly, labor-intensive, and time-consuming experimental methods. Most of the existing methods can only be used to deal with the ion channels of knowing 3D structures; however, the 3D structures of most ion channels are still unknown. Many predictors based on protein sequence were developed to address the challenge, while most of their results need to be improved, or predicting web servers are missing. In this paper, a sequence-based classifier, called “iCDI-W2vCom,” was developed to identify the interactions between ion channels and drugs. In the predictor, the drug compound was formulated by SMILES-word2vec, FP2-word2vec, SMILES-node2vec, and ECFPs via a 1184D vector, ion channel was represented by the word2vec via a 64D vector, and the prediction engine was operated by the LightGBM classifier. The accuracy and AUC achieved by iCDI-W2vCom via the fivefold cross validation were 91.95% and 0.9703, which outperformed other existing predictors in this area. A user-friendly web server for iCDI-W2vCom was established at http://www.jci-bioinfo.cn/icdiw2v. The proposed method may also be a potential method for predicting target–drug interaction.

Simulation and Emulation Tools for Fog Computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Simar Preet Singh ◽  
Rajesh Kumar ◽  
Anju Sharma ◽  
S. Raji Reddy ◽  
Priyanka Vashisht
Keyword(s):  
Quality Of Service ◽  
Programming Languages ◽  
Traffic Management ◽  
Fog Computing ◽  
Research Work ◽  
Research Experience ◽  
Computing Paradigm ◽  
Quality Of Service Parameters ◽  
User Friendly

Background: Fog computing paradigm has recently emerged and gained higher attention in present era of Internet of Things. The growth of large number of devices all around, leads to the situation of flow of packets everywhere on the Internet. To overcome this situation and to provide computations at network edge, fog computing is the need of present time that enhances traffic management and avoids critical situations of jam, congestion etc. Methods: For research purposes, there are many methods to implement the scenarios of fog computing i.e. real-time implementation, implementation using emulators, implementation using simulators etc. The present study aims to describe the various simulation and emulation tools for implementing fog computing scenarios. Results: Review shows that iFogSim is the simulator that most of the researchers use in their research work. Among emulators, EmuFog is being used at higher pace than other available emulators. This might be due to ease of implementation and user-friendly nature of these tools and language these tools are based upon. The use of such tools enhance better research experience and leads to improved quality of service parameters (like bandwidth, network, security etc.). Conclusion: There are many fog computing simulators/emulators based on many different platforms that uses different programming languages. The paper concludes that the two main simulation and emulation tools in the area of fog computing are iFogSim and EmuFog. Accessibility of these simulation/emulation tools enhance better research experience and leads to improved quality of service parameters along with the ease of their usage.

scholarly journals An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zi-Ji Yan
Keyword(s):  
Ion Channel ◽  
Extreme Learning Machine ◽  
Nuclear Receptor ◽  
Drug Target ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Weighted Extreme Learning Machine ◽  
Speed Up ◽  
Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

scholarly journals TOK Homologue in Neurospora crassa: First Cloning and Functional Characterization of an Ion Channel in a Filamentous Fungus

2003 ◽  
Vol 2 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Stephen K. Roberts
Keyword(s):  
Ion Channels ◽  
Ion Channel ◽  
Neurospora Crassa ◽  
Filamentous Fungus ◽  
Single Channel ◽  
Functional Characterization ◽  
Reversal Potential ◽  
Channel Activity ◽  
Biophysical Properties ◽  
Patch Clamp Technique

ABSTRACT In contrast to animal and plant cells, very little is known of ion channel function in fungal physiology. The life cycle of most fungi depends on the “filamentous” polarized growth of hyphal cells; however, no ion channels have been cloned from filamentous fungi and comparatively few preliminary recordings of ion channel activity have been made. In an attempt to gain an insight into the role of ion channels in fungal hyphal physiology, a homolog of the yeast K+ channel (ScTOK1) was cloned from the filamentous fungus, Neurospora crassa. The patch clamp technique was used to investigate the biophysical properties of the N. crassa K+ channel (NcTOKA) after heterologous expression of NcTOKA in yeast. NcTOKA mediated mainly time-dependent outward whole-cell currents, and the reversal potential of these currents indicated that it conducted K+ efflux. NcTOKA channel gating was sensitive to extracellular K+ such that channel activation was dependent on the reversal potential for K+. However, expression of NcTOKA was able to overcome the K+ auxotrophy of a yeast mutant missing the K+ uptake transporters TRK1 and TRK2, suggesting that NcTOKA also mediated K+ influx. Consistent with this, close inspection of NcTOKA-mediated currents revealed small inward K+ currents at potentials negative of EK. NcTOKA single-channel activity was characterized by rapid flickering between the open and closed states with a unitary conductance of 16 pS. NcTOKA was effectively blocked by extracellular Ca2+, verapamil, quinine, and TEA+ but was insensitive to Cs+, 4-aminopyridine, and glibenclamide. The physiological significance of NcTOKA is discussed in the context of its biophysical properties.

Ion channel activities of cultured rat mesangial cells

1991 ◽  
Vol 261 (5) ◽  
pp. F808-F814 ◽  
Author(s):  
H. Matsunaga ◽  
N. Yamashita ◽  
Y. Miyajima ◽  
T. Okuda ◽  
H. Chang ◽  
...  
Keyword(s):  
Angiotensin Ii ◽  
Ion Channels ◽  
Patch Clamp ◽  
Ion Channel ◽  
Arginine Vasopressin ◽  
Mesangial Cells ◽  
Cation Channel ◽  
K Channel ◽  
Patch Clamp Technique ◽  
Inside Out

We used the patch-clamp technique to clarify the nature of ion channels in renal mesangial cells in culture. In the cell-attached mode most patches were silent in the absence of agonists. In some patches a 25-pS nonselective channel was observed. This 25-pS cation channel was consistently observed in inside-out patches, and it was activated by intracellular Ca2+. Excised patch experiments also revealed the existence of a 40-pS K+ channel, which was activated by intracellular Ca2+. This 40-pS K+ channel was observed infrequently in the cell-attached mode. The activities of both channels were increased by arginine vasopressin or angiotensin II, resulting from an increase in intracellular Ca2+ concentration.

scholarly journals Proper Voltage-Dependent Ion Channel Function in Dysferlin-Deficient Cardiomyocytes

2015 ◽  
Vol 36 (3) ◽  
pp. 1049-1058 ◽  
Author(s):  
Lena Rubi ◽  
Vaibhavkumar S. Gawali ◽  
Helmut Kubista ◽  
Hannes Todt ◽  
Karlheinz Hilber ◽  
...  
Keyword(s):  
Ion Channels ◽  
Muscular Dystrophy ◽  
Ion Channel ◽  
Cardiac Electrophysiology ◽  
Cardiac Complications ◽  
Membrane Repair ◽  
Patch Clamp Technique ◽  
Cardiac Ion Channels ◽  
Ventricular Cardiomyocytes ◽  
Voltage Dependent

Background/Aims: Dysferlin plays a decisive role in calcium-dependent membrane repair in myocytes. Mutations in the encoding DYSF gene cause a number of myopathies, e.g. limb-girdle muscular dystrophy type 2B (LGMD2B). Besides skeletal muscle degenerative processes, dysferlin deficiency is also associated with cardiac complications. Thus, both LGMD2B patients and dysferlin-deficient mice develop a dilated cardiomyopathy. We and others have recently reported that dystrophin-deficient ventricular cardiomyocytes from mouse models of Duchenne muscular dystrophy show significant abnormalities in voltage-dependent ion channels, which may contribute to the pathophysiology in dystrophic cardiomyopathy. The aim of the present study was to investigate if dysferlin, like dystrophin, is a regulator of cardiac ion channels. Methods and Results: By using the whole cell patch-clamp technique, we compared the properties of voltage-dependent calcium and sodium channels, as well as action potentials in ventricular cardiomyocytes isolated from the hearts of normal and dysferlin-deficient (dysf) mice. In contrast to dystrophin deficiency, the lack of dysferlin did not impair the ion channel properties and left action potential parameters unaltered. In connection with normal ECGs in dysf mice these results suggest that dysferlin deficiency does not perturb cardiac electrophysiology. Conclusion: Our study demonstrates that dysferlin does not regulate cardiac voltage-dependent ion channels, and implies that abnormalities in cardiac ion channels are not a universal characteristic of all muscular dystrophy types.

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

2020 ◽  
Vol 55 (S3) ◽  
pp. 14-45
Keyword(s):  
Artificial Intelligence ◽  
Molecular Dynamics ◽  
Ion Channels ◽  
Ion Channel ◽  
Computational Methods ◽  
Homology Modelling ◽  
Channel Structure ◽  
Learning Approaches ◽  
Qsar Analysis ◽  
Wide Range

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 have evolved into important and invaluable approaches for studying ion channels and their functions. This is mainly due to their demanding mechanism of action where a static picture of an ion channel structure is often insufficient to fully understand the underlying mechanism. Therefore, the use of computational methods is as important as chemical-biological based experimental methods for a better understanding of ion channels. This review provides an overview on a variety of computational methods and software specific to the field of ion-channels. Artificial intelligence (or more precisely machine learning) approaches are applied for the sequence-based prediction of ion channel family, or topology of the transmembrane region. In case sufficient data on ion channel modulators is available, these methods can also be applied for quantitative structureactivity relationship (QSAR) analysis. Molecular dynamics (MD) simulations combined with computational molecular design methods such as docking can be used for analysing the function of ion channels including ion conductance, different conformational states, binding sites and ligand interactions, and the influence of mutations on their function. In the absence of a three-dimensional protein structure, homology modelling can be applied to create a model of your ion channel structure of interest. Besides highlighting a wide range of successful applications, we will also provide a basic introduction to the most important computational methods and discuss best practices to get a rough idea of possible applications and risks.

scholarly journals TACAN is an essential component of the mechanosensitive ion channel responsible for pain sensing

2018 ◽  
Author(s):  
L. Beaulieu-Laroche ◽  
M. Christin ◽  
AM Donoghue ◽  
F. Agosti ◽  
N. Yousefpour ◽  
...  
Keyword(s):  
Ion Channels ◽  
Heterologous Expression ◽  
Ion Channel ◽  
Behavioral Responses ◽  
Mechanical Stimuli ◽  
Thermal Pain ◽  
Fundamental Process ◽  
Mechanosensitive Ion Channel ◽  
Pain Stimuli ◽  
Essential Subunit

SummaryMechanotransduction, the conversion of mechanical stimuli into electrical signals, is a fundamental process underlying several physiological functions such as touch and pain sensing, hearing and proprioception. This process is carried out by specialized mechanosensitive ion channels whose identities have been discovered for most functions except pain sensing. Here we report the identification of TACAN (Tmem120A), an essential subunit of the mechanosensitive ion channel responsible for sensing mechanical pain. TACAN is expressed in a subset of nociceptors, and its heterologous expression increases mechanically-evoked currents in cell lines. Purification and reconstitution of TACAN in synthetic lipids generates a functional ion channel. Finally, knocking down TACAN decreases the mechanosensitivity of nociceptors and reduces behavioral responses to mechanical but not to thermal pain stimuli, without affecting the sensitivity to touch stimuli. We propose that TACAN is a pore-forming subunit of the mechanosensitive ion channel responsible for sensing mechanical pain.

scholarly journals Tarantula toxins use common surfaces for interacting with Kv and ASIC ion channels

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Kanchan Gupta ◽  
Maryam Zamanian ◽  
Chanhyung Bae ◽  
Mirela Milescu ◽  
Dmitriy Krepkiy ◽  
...  
Keyword(s):  
Ion Channels ◽  
Ion Channel ◽  
Extracellular Domain ◽  
Voltage Sensor ◽  
Concave Surface ◽  
Voltage Sensing ◽  
Membrane Interaction ◽  
Computational Approaches ◽  
Physical Environments

Tarantula toxins that bind to voltage-sensing domains of voltage-activated ion channels are thought to partition into the membrane and bind to the channel within the bilayer. While no structures of a voltage-sensor toxin bound to a channel have been solved, a structural homolog, psalmotoxin (PcTx1), was recently crystalized in complex with the extracellular domain of an acid sensing ion channel (ASIC). In the present study we use spectroscopic, biophysical and computational approaches to compare membrane interaction properties and channel binding surfaces of PcTx1 with the voltage-sensor toxin guangxitoxin (GxTx-1E). Our results show that both types of tarantula toxins interact with membranes, but that voltage-sensor toxins partition deeper into the bilayer. In addition, our results suggest that tarantula toxins have evolved a similar concave surface for clamping onto α-helices that is effective in aqueous or lipidic physical environments.

scholarly journals Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Membranes ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 672
Author(s):  
Md. Ashrafuzzaman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Ion Channels ◽  
Ion Channel ◽  
Research Trend ◽  
Cellular Processes ◽  
Research Areas ◽  
Huge Data ◽  
Functional Aspects

Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.

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