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Int. J. Mol. Sci. 2017, 18(9), 1838; doi:10.3390/ijms18091838

IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types

1
Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
2
Development and Planning Department, Inner Mongolia University, Hohhot 010021, China
3
Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China
4
Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
*
Authors to whom correspondence should be addressed.
Received: 7 August 2017 / Revised: 21 August 2017 / Accepted: 21 August 2017 / Published: 24 August 2017
(This article belongs to the Special Issue Special Protein Molecules Computational Identification)
View Full-Text   |   Download PDF [942 KB, uploaded 24 August 2017]   |  

Abstract

Ion channels (IC) are ion-permeable protein pores located in the lipid membranes of all cells. Different ion channels have unique functions in different biological processes. Due to the rapid development of high-throughput mass spectrometry, proteomic data are rapidly accumulating and provide us an opportunity to systematically investigate and predict ion channels and their types. In this paper, we constructed a support vector machine (SVM)-based model to quickly predict ion channels and their types. By considering the residue sequence information and their physicochemical properties, a novel feature-extracted method which combined dipeptide composition with the physicochemical correlation between two residues was employed. A feature selection strategy was used to improve the performance of the model. Comparison results of in jackknife cross-validation demonstrated that our method was superior to other methods for predicting ion channels and their types. Based on the model, we built a web server called IonchanPred which can be freely accessed from http://lin.uestc.edu.cn/server/IonchanPredv2.0. View Full-Text
Keywords: ion channels; pseudo-dipeptide composition; machine learning method ion channels; pseudo-dipeptide composition; machine learning method
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhao, Y.-W.; Su, Z.-D.; Yang, W.; Lin, H.; Chen, W.; Tang, H. IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types. Int. J. Mol. Sci. 2017, 18, 1838.

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