Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 18, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/ijms18091838
Keywords
ion channels; pseudo-dipeptide composition; machine learning method
Funding
- Applied Basic Research Program of Sichuan Province [2015JY0100, 14JC0121]
- Fundamental Research Funds for the Central Universities of China [ZYGX2015J144, ZYGX2015Z006, ZYGX2016J118, ZYGX2016J125, ZYGX2016J126]
- Program for the Top Young Innovative Talents of Higher Learning Institutions of Hebei Province [BJ2014028]
- Outstanding Youth Foundation of North China University of Science and Technology [JP201502]
- China Postdoctoral Science Foundation [2015M582533]
- Scientific Research Foundation of the Education Department of Sichuan Province [11ZB122]
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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.
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