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

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...

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Bibliographic Details
Published inInternational journal of molecular sciences Vol. 18; no. 9; p. 1838
Main Authors Zhao, Ya-Wei, Su, Zhen-Dong, Yang, Wuritu, Lin, Hao, Chen, Wei, Tang, Hua
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 24.08.2017
MDPI
Subjects
Algorithms
Biological activity
Channel pores
Computational Biology - methods
Databases, Protein
Dipeptides - chemistry
Dipeptides - metabolism
Internet
Ion channels
Ion Channels - chemistry
Ion Channels - metabolism
Ions
Lipid membranes
machine learning method
Mass spectrometry
Mass spectroscopy
Physicochemical properties
pseudo-dipeptide composition
Reproducibility of Results
Servers
Software
Support Vector Machine
Support vector machines
Workflow
pseudo-dipeptide composition
machine learning method
ion channels
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Summary: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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ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms18091838