LiGIoNs: A computational method for the detection and classification of ligand-gated ion channels
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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 journal › Journal article › peer-review
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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