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

Ashrafuzzaman, Md.

doi:10.3390/membranes11090672

Cited by 17 publications

(17 citation statements)

References 84 publications

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“…Taju et al has used DL methods for the classification of ion transporters, and ion channels from membrane proteins, by training the deep neural networks using the position-specific scoring matrix profile as the input [92]. ML has been used to derive the feature vectors of ion channels including SVMProt, and k-skip-n-gram methods, 188-, and 400 dimensional features, respectively [93].…”

Section: Multiomics and Proteomic Data Integrationmentioning

confidence: 99%

Perspective Chapter: Pattern Recognition for Mass-Spectrometry-Based Proteomics

Bangert¹,

Balasubramaniam²,

Parker³

et al. 2024

Biomedical Engineering

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Multiomic analysis comprises genomics, proteomics, and metabolomics leads to meaningful insights but necessitates sifting through voluminous amounts of complex data. Proteomics in particular focuses on the end product of gene expression – i.e., proteins. The mass spectrometric approach has proven to be a workhorse for the qualitative and quantitative study of protein interactions as well as post-translational modifications (PTMs). A key component of mass spectrometry (MS) is spectral data analysis, which is complex and has many challenges as it involves identifying patterns across a multitude of spectra in combination with the meta-data related to the origin of the spectrum. Artificial Intelligence (AI) along with Machine Learning (ML), and Deep Learning (DL) algorithms have gained more attention lately for analyzing the complex spectral data to identify patterns and to create networks of value for biomarker discovery. In this chapter, we discuss the nature of MS proteomic data, the relevant AI methods, and demonstrate their applicability. We also show that AI can successfully identify biomarkers and aid in the diagnosis, prognosis, and treatment of specific diseases.

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Section: Multiomics and Proteomic Data Integrationmentioning

confidence: 99%

Perspective Chapter: Pattern Recognition for Mass-Spectrometry-Based Proteomics

Bangert¹,

Balasubramaniam²,

Parker³

et al. 2024

Biomedical Engineering

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“…In the realm of drug discovery, ion channels represent promising targets for therapeutic intervention, as their modulation can result in changes in cellular behavior [6,7]. However, the high cost and time required for wet lab experiments to characterize ion channels has spurred the development of computational methods for this purpose [8,9]. These methods can greatly accelerate the drug discovery process by providing efficient and cost-effective predictions of ion channel presence and function.…”

Section: Introductionmentioning

confidence: 99%

“…There has been a significant amount of research on predicting ion channel proteins in the past, with an emphasis on developing computational methods that can accurately differentiate ion channels from non-ion channels [8][9][10][11][12][13][14]. These methods have often utilized traditional machine learning techniques, such as support vector machines (SVM) and random forests (RF), which classify protein sequences based on features derived from their primary, secondary, and tertiary structures.…”

Section: Introductionmentioning

confidence: 99%

“…2 of 20 as structural features, such as secondary structure elements or solvent accessibility [9,15]. The use of these features for ion channel prediction is thoroughly explained in Menke et al [9] and Ashrafuzzaman [8]. However, the emergence of deep learning has provided new opportunities for ion channel prediction, with recent studies demonstrating the potential of these techniques to generate rich representations of protein sequences and enhance the performance of ion channel predictors [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Discriminating Ion Channels from Non-Ion Channel Membrane Proteins Using Machine Learning and Deep Learning Classifiers with Protein Language Model Representations

Ghazikhani¹,

Butler²

2023

Preprint

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Ion channels are integral membrane proteins that facilitate the movement of ions across cell membranes, playing a key role in a range of biological processes. The high cost and time required for wet lab experiments to characterize ion channels has spurred the development of computational methods for this purpose. In our previous work, we demonstrated the effectiveness of protein language models for ion channel prediction, using a logistic regression classifier to distinguish ion channels from non-ion channels (TooT-BERT-C) and transporters from non-transporters (TooT-BERT-T). In this study, we build upon this approach by using a combination of classical machine learning classifiers and a Convolutional Neural Network (CNN) with fine-tuned representations from ProtBERT, ProtBERT-BFD, and MembraneBERT to discriminate ion channels from non-ion channels. The results of our experiments demonstrate that TooT-BERT-CNN-C, a combination of the representations from ProtBERT-BFD and a CNN, outperforms existing state-of-the-art methods for predicting ion channels, with a Matthews Correlation Coefficient (MCC) of 0.86 and an accuracy of 98.35% on an independent test set.

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“…The main advantages of the AI approach in the context of functional analysis of ion channels are that it is directly signal-based and neither requires knowledge about the mechanism of channel gating nor expertise in statistical description of gating kinetics. In a broader context of the research on ion channels, the AI techniques can be also successfully exploited in the channel’s proteomics and genomics, as discussed in [ 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach

et al. 2022

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The large conductance voltage- and Ca2+-activated K+ channels from the inner mitochondrial membrane (mitoBK) are modulated by a number of factors. Among them flavanones, including naringenin (Nar), arise as a promising group of mitoBK channel regulators from a pharmacological point of view. It is well known that in the presence of Nar the open state probability (pop) of mitoBK channels significantly increases. Nevertheless, the molecular mechanism of the mitoBK-Nar interactions remains still unrevealed. It is also not known whether the effects of naringenin administration on conformational dynamics can resemble those which are exerted by the other channel-activating stimuli. In aim to answer this question, we examine whether the dwell-time series of mitoBK channels which were obtained at different voltages and Nar concentrations (yet allowing to reach comparable pops) are discernible by means of artificial intelligence methods, including k-NN and shapelet learning. The obtained results suggest that the structural complexity of the gating dynamics is shaped both by the interaction of channel gate with the voltage sensor (VSD) and the Nar-binding site. For a majority of data one can observe stimulus-specific patterns of channel gating. Shapelet algorithm allows to obtain better prediction accuracy in most cases. Probably, because it takes into account the complexity of local features of a given signal. About 30% of the analyzed time series do not sufficiently differ to unambiguously distinguish them from each other, which can be interpreted in terms of the existence of the common features of mitoBK channel gating regardless of the type of activating stimulus. There exist long-range mutual interactions between VSD and the Nar-coordination site that are responsible for higher levels of Nar-activation (Δpop) at deeply depolarized membranes. These intra-sensor interactions are anticipated to have an allosteric nature.

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

Cited by 17 publications

References 84 publications

Perspective Chapter: Pattern Recognition for Mass-Spectrometry-Based Proteomics

Perspective Chapter: Pattern Recognition for Mass-Spectrometry-Based Proteomics

Discriminating Ion Channels from Non-Ion Channel Membrane Proteins Using Machine Learning and Deep Learning Classifiers with Protein Language Model Representations

To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach

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