An efficient approach for the prediction of ion channels and their subfamilies |
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| Institution: | 1. Center for Advanced Bioinformatics & Systems Medicine, Sookmyung Women’s University, Seoul 140-742, Republic of Korea;2. Discovery Biology Group, Institute Pasteur Korea, Seongnam-si, Gyeonggi-do 463-400, Republic of Korea;3. Imaging Processing Platform, Institute Pasteur Korea, Seongnam-si, Gyeonggi-do 463-400, Republic of Korea;4. Department of Biological Sciences, Sookmyung Women’s University, Seoul 140-742, Republic of Korea;1. Research Center for Solar Energy Chemistry, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan;2. Department of Bioinformatics, College of Life Sciences, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan;3. Environmental and Materials Chemistry Course, Osaka Prefecture University College of Technology, 26-12 Saiwai, Neyagawa, Osaka 572-8572, Japan;1. Department of Chemistry, Suleyman Demirel University, 32260 Isparta, Turkey;2. Department of Polymer Engineering, Karabuk University, 78050 Karabuk, Turkey;3. Instituto de Estructura de la Materia, IEM-CSIC, Serrano 123, 28006 Madrid, Spain;4. Unidad Asociada Química Física UCM/IEM-CSIC, Departamento de Química Física I, Universidad Complutense, 28040 Madrid, Spain;1. Department of Chemistry, Rabigh College of Science and Art, King Abdulaziz University, Saudi Arabia;2. Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh 11451, Saudi Arabia;3. Department of Chemistry, Faculty of Science, Alexandria University, P.O. Box 426 Ibrahimia, 21321 Alexandria, Egypt |
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| Abstract: | Ion channels are integral membrane proteins that are responsible for controlling the flow of ions across the cell. There are various biological functions that are performed by different types of ion channels. Therefore for new drug discovery it is necessary to develop a novel computational intelligence techniques based approach for the reliable prediction of ion channels families and their subfamilies. In this paper random forest based approach is proposed to predict ion channels families and their subfamilies by using sequence derived features. Here, seven feature vectors are used to represent the protein sample, including amino acid composition, dipeptide composition, correlation features, composition, transition and distribution and pseudo amino acid composition. The minimum redundancy and maximum relevance feature selection is used to find the optimal number of features for improving the prediction performance. The proposed method achieved an overall accuracy of 100%, 98.01%, 91.5%, 93.0%, 92.2%, 78.6%, 95.5%, 84.9%, MCC values of 1.00, 0.92, 0.88, 0.88, 0.90, 0.79, 0.91, 0.81 and ROC area values of 1.00, 0.99, 0.99, 0.99, 0.99, 0.95, 0.99 and 0.96 using 10-fold cross validation to predict the ion channels and non-ion channels, voltage gated ion channels and ligand gated ion channels, four subfamilies (calcium, potassium, sodium and chloride) of voltage gated ion channels, and four subfamilies of ligand gated ion channels and predict subfamilies of voltage gated calcium, potassium, sodium and chloride ion channels respectively. |
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| Keywords: | Ion channels Voltage gated ion channels Ligand gated ion channels Random forest Minimum redundancy maximum relevance Sodium Potassium Calcium Chloride |
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