| Prediction of GABA(A) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. | |
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MedLine Citation:
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PMID: 21536049 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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The amino acid gamma-aminobutyric-acid receptors (GABA(A)Rs) belong to the ligand-gated ion channels (LGICs) superfamily. GABA(A)Rs are highly diverse in the central nervous system. These channels play a key role in regulating behavior. As a result, the prediction of GABA(A)Rs from the amino acid sequence would be helpful for research on these receptors. We have developed a method to predict these proteins using the features obtained from Chou's pseudo-amino acid composition concept and support vector machine as a powerful machine learning approach. The predictor efficiency was assessed by five-fold cross-validation. This method achieved an overall accuracy and Matthew's correlation coefficient (MCC) of 94.12% and 0.88, respectively. Furthermore, to evaluate the effect and power of each feature, the minimum Redundancy and Maximum Relevance (mRMR) feature selection method was implemented. An interesting finding in this study is the presence of all six characters (hydrophobicity, hydrophilicity, side chain mass, pK1, pK2 and pI) or combination of the characters among the 5 higher ranked features (pk2 and pI, hydrophobicity and mass, pk1, hydrophilicity and mass) obtained from the mRMR feature selection method. The results show a biologically justifiable ranked attributes of pk2 and pI; hydrophobicity, hydrophilicity and mass; mass and pk1; pk2 and mass. Based on our results, using the concept of Chou's pseudo-amino acid composition and support vector machine is an effective approach for the prediction of GABA(A)Rs. |
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Authors:
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Hassan Mohabatkar; Majid Mohammad Beigi; Abolghasem Esmaeili |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-4-29 |
Journal Detail:
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Title: Journal of theoretical biology Volume: - ISSN: 1095-8541 ISO Abbreviation: - Publication Date: 2011 Apr |
Date Detail:
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Created Date: 2011-5-3 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0376342 Medline TA: J Theor Biol Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2011. Published by Elsevier Ltd. |
Affiliation:
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Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran; Department of Biology, College of Sciences, Shiraz University, Shiraz 71454, Iran. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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