An efficient approach for the prediction of ion channels and their subfamilies

Tiwari, Arvind Kumar; Srivastava, Rajeev

doi:10.1016/j.compbiolchem.2015.07.002

Cited by 9 publications

(17 citation statements)

References 53 publications

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“…LGIC subfamily Subunit Count Considering the importance of ion channels for normal cellular function and their designated role as drug targets [18,19], several methods have been developed for the prediction of these proteins using information encoded exclusively in their amino acid sequence [20][21][22][23][24][25][26][27]. Ion channel prediction is mainly based on SVMs, and several feature selection techniques have been used to train the machine learning classifiers, mainly for the prediction and classification of VGICs.…”

Section: Many Genes Encodementioning

confidence: 99%

“…The main drawback of all methods is that the informative parameters used for training are amino acid, dipeptide and tripeptide compositions, while the special topological features of ion channels are not taken into consideration during method development. On top of that, while IonChanPred [21], PSIONplus [22] and two recently developed machine learning classifiers by Tiwari and Srivastava [27] and by Han et al [26] are the only methods that can detect the class of LGICs, neither those, nor any other method classifies…”

Section: Many Genes Encodementioning

confidence: 99%

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LiGIoNs: A Computational Method for the Detection and Classification of Ligand-Gated Ion Channels

Nastou¹,

Petichakis

Litou

et al. 2019

Preprint

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Ligand-Gated Ion Channels (LGICs) are 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 amino acid composition. However, special topological characteristics of these proteins have not been utilized to date, which results in weaknesses regarding the correct class categorization of predicted proteins. Here we report the development of LiGIoNs, a profile Hidden Markov Model (pHMM) method for the prediction and ligand-based classification of LGICs, utilizing their special topological characteristics. The method consists of a library of 35 pHMMs, built from the alignment of transmembrane segments of representative LGIC sequences. In addition,14 Pfam pHMMs are used to further annotate and correctly classify unknown protein sequences into one of the 10 LGIC subfamilies. Evaluation of the method showed that it outperforms existent 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/.

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Section: Many Genes Encodementioning

confidence: 99%

Section: Many Genes Encodementioning

confidence: 99%

LiGIoNs: A Computational Method for the Detection and Classification of Ligand-Gated Ion Channels

Nastou¹,

Petichakis

Litou

et al. 2019

Preprint

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“…This energy is also required to push the ion/molecule to the other side of the cell membrane from a region of lower concentration to a region of higher concentration . Furthermore, an ion channel is a transmembrane protein complex located in the lipid bilayer membrane of all cells . Ion channels are also utilized as targets for disease diagnosis and drug development .…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, an ion channel is a transmembrane protein complex located in the lipid bilayer membrane of all cells . Ion channels are also utilized as targets for disease diagnosis and drug development . In contrast with ion transporters, ion channels perform passive transport by moving/gating the flow of ions across the cell membrane through channels down their concentration gradient or through facilitated diffusion .…”

Section: Introductionmentioning

confidence: 99%

“…Ion channels are also utilized as targets for disease diagnosis and drug development . In contrast with ion transporters, ion channels perform passive transport by moving/gating the flow of ions across the cell membrane through channels down their concentration gradient or through facilitated diffusion . Channels are generally part of pores in a cell membrane used as a gate and only open or close in response to a specific stimulus, such as a chemical or electrical signal .…”

Section: Introductionmentioning

confidence: 99%

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DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

Taju

2019

J Comput Chem

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The movement of ions across the cell membrane is an essential for many biological processes. This study is focused on ion channels and ion transporters (pumps) as types of border guards control the incessant traffic of ions across cell membranes. Ion channels and ion transporters function to regulate membrane potential and electrical signaling and play important roles in cell proliferation, migration, apoptosis, and differentiation. In their behaviors, it is found that ion channels differ significantly from ion transporters. Therefore, a method for automatically classifying ion transporters and ion channels from membrane proteins is proposed by training deep neural networks and using the position‐specific scoring matrix profile as an input. The key of novelty is the three‐stage approach, in which five techniques for data normalization are used; next three imbalanced data techniques are applied to the minority classes and then, six classifiers are compared with the proposed method. © 2019 Wiley Periodicals, Inc.

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Machine learning‐based approach for prediction of ion channels and their subclasses

Singh¹,

Tiwari²

2022

J of Cellular Biochemistry

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Ion channels are ion-permeable protein pores that are found in all cell lipid membranes. Distinct ion channels play multiple roles in biological processes.Proteomic data is fast accumulating as a result of the fast growth of mass spectrometry and giving us the chance to comprehensively explore ion channel classes along with their subclasses. This paper proposes an eXtreme Gradient Boosting (XGBoost)-based method to estimate the ion channel classes and their subclasses. Here, 12 feature vectors are applied to better characterize protein sequences like amino acid composition, pseudo-amino acid composition, normalized moreau-broto autocorrelation, amphiphilic pseudo-amino acid composition, dipeptide composition, Geary autocorrelation, tripeptide composition, sequence-order-coupling number, composition/transition/distribution, conjoint triad, moran autocorrelation, quasisequence-order descriptors. Here, a total of 9920 features are extracted from the protein sequence. The principal component analysis is applied to determine the optimal number of features to optimize the performance. In 10-fold cross-validation the proposed XGBoost based approach with optimal 50 features achieved accuracy of 100%, 98.70%, 98.77%, 97.26%, 87.40%, 97.39%, 98.03%, 96.42%, and F1-Score of 100%, 99%, 99%, 97%, 87%, 97%, 98%, 97%, for prediction of ion channel and nonion channel, voltage-gated and ligand-gated ion channels, subclasses of voltage-gated ion channels (VGICs), subclasses of ligand-gated ion channels (LGICs), subclasses of voltage-gated calcium channels (VGCCs), subclasses of voltage-gated potassium channels (VGKCs), subclasses of voltage-gated sodium channels (VGSCs), and subclasses of voltage-gated chloride channels, respectively. Here the proposed approach also compares with the other approaches such as support vector machine, k-nearest neighbor, Gaussian Naïve Bayes, and random forest and also compares with existing methods such as support vector machine (SVM) with maximum relevance maximum distance with an accuracy of 86.6%, 83.7%, and 85.1%, for ion channels, non-ion channels and overall respectively and SVM with radial basis function kernel-based method with an accuracy of 100%, 97% and 99.9% for ion channels, nonion channels, and overall accuracy, respectively.

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An efficient approach for the prediction of ion channels and their subfamilies

Cited by 9 publications

References 53 publications

LiGIoNs: A Computational Method for the Detection and Classification of Ligand-Gated Ion Channels

LiGIoNs: A Computational Method for the Detection and Classification of Ligand-Gated Ion Channels

DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

Machine learning‐based approach for prediction of ion channels and their subclasses

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