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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| Published in | International journal of molecular sciences Vol. 18; no. 9; p. 1838 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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24.08.2017
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| 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. |
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| AbstractList | 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. 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 . |
| Author | Chen, Wei Tang, Hua Lin, Hao Su, Zhen-Dong Yang, Wuritu Zhao, Ya-Wei |
| AuthorAffiliation | 4 Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China 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; lianyingteng@hotmail.com (Y.-W.Z.); zhendong_su@163.com (Z.-D.S.); wyang@imu.edu.cn (W.Y.) 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 |
| AuthorAffiliation_xml | – name: 3 Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China – name: 2 Development and Planning Department, Inner Mongolia University, Hohhot 010021, China – name: 4 Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China – name: 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; lianyingteng@hotmail.com (Y.-W.Z.); zhendong_su@163.com (Z.-D.S.); wyang@imu.edu.cn (W.Y.) |
| Author_xml | – sequence: 1 givenname: Ya-Wei surname: Zhao fullname: Zhao, Ya-Wei email: lianyingteng@hotmail.com organization: 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. lianyingteng@hotmail.com – sequence: 2 givenname: Zhen-Dong surname: Su fullname: Su, Zhen-Dong email: zhendong_su@163.com organization: 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. zhendong_su@163.com – sequence: 3 givenname: Wuritu surname: Yang fullname: Yang, Wuritu email: wyang@imu.edu.cn, wyang@imu.edu.cn organization: Development and Planning Department, Inner Mongolia University, Hohhot 010021, China. wyang@imu.edu.cn – sequence: 4 givenname: Hao surname: Lin fullname: Lin, Hao email: hlin@uestc.edu.cn organization: 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. hlin@uestc.edu.cn – sequence: 5 givenname: Wei surname: Chen fullname: Chen, Wei email: greatchen@ncst.edu.cn, greatchen@ncst.edu.cn organization: Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China. greatchen@ncst.edu.cn – sequence: 6 givenname: Hua surname: Tang fullname: Tang, Hua email: tanghua771211@aliyun.com organization: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China. tanghua771211@aliyun.com |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28837067$$D View this record in MEDLINE/PubMed |
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| StartPage | 1838 |
| SubjectTerms | 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 |
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| Title | IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types |
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