LiGIoNs: Prediction of Ligand Gated Ion Channels


During the last few years a couple of computational methods have been developed to predict ion channels and classify them by their gating mechanism. Most of the methods rely on machine learning algorithms, like support vector machines and random forests, which utilize features extracted by protein sequence characteristics. However, there is currently no method available to predict and classify Ligand-Gated Ion Channels specifically.
LiGIoNs is a profile Hidden Markov Model based method capable of predicting Ligand-Gated Ion Channels. The method consists of a library of 10 pHMMs, each corresponding to a single LGIC subfamily. In addition, 14 Pfam pHMMs are used to further annotate and correctly classify unknown protein sequences into one of the 10 LGIC subfamilies.

Additional Information

Supplementary Data including scripts for running LiGIoNs locally and recreating the pHMMs, as well as the results from the application of LiGIoNs on the UniProt reference proteomes, can be found in a GitHub repository here. Additionally, the test datasets, the hmm files and the proteome results can be directly downloaded from here.

Current Version of LiGIoNs: v1.0

Avgi E. Apostolakou, Katerina C. Nastou, Georgios N. Petichakis, Zoi I. Litou and Vassiliki A. Iconomidou
LiGIoNs: Α Computational Method for the Detection and Classification of Ligand-Gated Ion Channels
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