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

Research output: Contribution to journalJournal articlepeer-review

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LiGIoNs : A computational method for the detection and classification of ligand-gated ion channels. / Apostolakou, Avgi E.; Nastou, Katerina C.; Petichakis, Georgios N.; Litou, Zoi I.; Iconomidou, Vassiliki A.

In: Biochimica et Biophysica Acta - Biomembranes, Vol. 1864, No. 9, 183956, 2022.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Apostolakou, AE, Nastou, KC, Petichakis, GN, Litou, ZI & Iconomidou, VA 2022, 'LiGIoNs: A computational method for the detection and classification of ligand-gated ion channels', Biochimica et Biophysica Acta - Biomembranes, vol. 1864, no. 9, 183956. https://doi.org/10.1016/j.bbamem.2022.183956

APA

Apostolakou, A. E., Nastou, K. C., Petichakis, G. N., Litou, Z. I., & Iconomidou, V. A. (2022). LiGIoNs: A computational method for the detection and classification of ligand-gated ion channels. Biochimica et Biophysica Acta - Biomembranes, 1864(9), [183956]. https://doi.org/10.1016/j.bbamem.2022.183956

Vancouver

Apostolakou AE, Nastou KC, Petichakis GN, Litou ZI, Iconomidou VA. LiGIoNs: A computational method for the detection and classification of ligand-gated ion channels. Biochimica et Biophysica Acta - Biomembranes. 2022;1864(9). 183956. https://doi.org/10.1016/j.bbamem.2022.183956

Author

Apostolakou, Avgi E. ; Nastou, Katerina C. ; Petichakis, Georgios N. ; Litou, Zoi I. ; Iconomidou, Vassiliki A. / LiGIoNs : A computational method for the detection and classification of ligand-gated ion channels. In: Biochimica et Biophysica Acta - Biomembranes. 2022 ; Vol. 1864, No. 9.

Bibtex

@article{7f5aa1fff0e5486a8041901219bf462d,
title = "LiGIoNs: A computational method for the detection and classification of ligand-gated ion channels",
abstract = "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/.",
keywords = "Ligand-gated ion channels, Membrane, Prediction, Profile hidden Markov models",
author = "Apostolakou, {Avgi E.} and Nastou, {Katerina C.} and Petichakis, {Georgios N.} and Litou, {Zoi I.} and Iconomidou, {Vassiliki A.}",
note = "Publisher Copyright: {\textcopyright} 2022",
year = "2022",
doi = "10.1016/j.bbamem.2022.183956",
language = "English",
volume = "1864",
journal = "B B A - Biomembranes",
issn = "0005-2736",
publisher = "Elsevier",
number = "9",

}

RIS

TY - JOUR

T1 - LiGIoNs

T2 - A computational method for the detection and classification of ligand-gated ion channels

AU - Apostolakou, Avgi E.

AU - Nastou, Katerina C.

AU - Petichakis, Georgios N.

AU - Litou, Zoi I.

AU - Iconomidou, Vassiliki A.

N1 - Publisher Copyright: © 2022

PY - 2022

Y1 - 2022

N2 - 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/.

AB - 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/.

KW - Ligand-gated ion channels

KW - Membrane

KW - Prediction

KW - Profile hidden Markov models

U2 - 10.1016/j.bbamem.2022.183956

DO - 10.1016/j.bbamem.2022.183956

M3 - Journal article

C2 - 35577076

AN - SCOPUS:85130607517

VL - 1864

JO - B B A - Biomembranes

JF - B B A - Biomembranes

SN - 0005-2736

IS - 9

M1 - 183956

ER -

ID: 311609883