Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine Article
Author(s): Mohabatkar H., Mohammad Beigi M., Esmaeili A.
Journal of Theoretical Biology Volume 281, Issue 1, 2011 , Pages 18-23
(IF=1.875 ,Q2,JCR2018) (CiteScore=2.04 ,Q1,Scopus2018)
The amino acid gamma-aminobutyric-acid receptors (GABAARs) belong to the ligand-gated ion channels (LGICs) superfamily. GABAARs are highly diverse in the central nervous system. These channels play a key role in regulating behavior. As a result, the prediction of GABAARs 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 GABAARs. © 2011.
Bioinformatics Matthew's correlation coefficient Minimum Redundancy and Maximum Relevance Protein family classification
Index Keywords:
4 aminobutyric acid A receptor algorithm amino acid bioinformatics classification correlation efficiency measurement ligand prediction protein accuracy amino acid composition article correlation coefficient hydrophilicity hydrophobicity molecular weight priority journal process development sequence analysis support vector machine validation process Algorithms Amino Acids Computational Biology Receptors, GABA-A Reproducibility of Results

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