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BioMed Research International
Volume 2016 (2016), Article ID 2375268, 12 pages
http://dx.doi.org/10.1155/2016/2375268
Research Article

In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches

1Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian 350122, China
2College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, Henan 471023, China
3School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
4College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
5School of Computer Science and Technology, Heilongjiang University, Harbin, Heilongjiang 150080, China
6School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China

Received 24 April 2016; Revised 8 June 2016; Accepted 19 June 2016

Academic Editor: Yungang Xu

Copyright © 2016 Zhijun Liao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. E. S. Locock, I. Yamamoto, P. Tran et al., “γ-aminobutyric acid(C) (GABAC) selective antagonists derived from the bioisosteric modification of 4-aminocyclopent-1-enecarboxylic acid: amides and hydroxamates,” Journal of Medicinal Chemistry, vol. 56, no. 13, pp. 5626–5630, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Mayerhofer, B. Höhne-Zell, K. Gamel-Didelon et al., “Gamma-aminobutyric acid (GABA): a para- and/or autocrine hormone in the pituitary,” The FASEB Journal, vol. 15, no. 6, pp. 1089–1091, 2001. View at Google Scholar · View at Scopus
  3. N. Okai, C. Takahashi, K. Hatada, C. Ogino, and A. Kondo, “Disruption of pknG enhances production of gamma-aminobutyric acid by Corynebacterium glutamicum expressing glutamate decarboxylase,” AMB Express, vol. 4, no. 1, article 20, pp. 1–8, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. F. C. Pereira, M. R. Rolo, E. Marques et al., “Acute increase of the glutamate-glutamine cycling in discrete brain areas after administration of a single dose of amphetamine,” Annals of the New York Academy of Sciences, vol. 1139, pp. 212–221, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Rigby, S. G. Cull-Candy, and M. Farrant, “Transmembrane AMPAR regulatory protein γ-2 is required for the modulation of GABA release by presynaptic AMPARs,” The Journal of Neuroscience, vol. 35, no. 10, pp. 4203–4214, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Zonouzi, J. Scafidi, P. Li et al., “GABAergic regulation of cerebellar NG2 cell development is altered in perinatal white matter injury,” Nature Neuroscience, vol. 18, no. 5, pp. 674–682, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Irie, R. Kikura-Hanajiri, M. Usami, N. Uchiyama, Y. Goda, and Y. Sekino, “MAM-2201, a synthetic cannabinoid drug of abuse, suppresses the synaptic input to cerebellar Purkinje cells via activation of presynaptic CB1 receptors,” Neuropharmacology, vol. 95, pp. 479–491, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. S. R. Zukin, A. B. Young, and S. H. Snyder, “Gamma-aminobutyric acid binding to receptor sites in the rat central nervous system,” Proceedings of the National Academy of Sciences of the United States of America, vol. 71, no. 12, pp. 4802–4807, 1974. View at Publisher · View at Google Scholar · View at Scopus
  9. R. W. Olsen and W. Sieghart, “International union of pharmacology. LXX. Subtypes of γ-aminobutyric acidA receptors: classification on the basis of subunit composition, pharmacology, and function. Update,” Pharmacological Reviews, vol. 60, no. 3, pp. 243–260, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. J.-M. Fritschy, “Epilepsy, E/I balance and GABAA receptor plasticity,” Frontiers in Molecular Neuroscience, vol. 1, article 5, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Mohabatkar, M. Mohammad Beigi, and A. Esmaeili, “Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine,” Journal of Theoretical Biology, vol. 281, no. 1, pp. 18–23, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. P. S. Miller and T. G. Smart, “Binding, activation and modulation of Cys-loop receptors,” Trends in Pharmacological Sciences, vol. 31, no. 4, pp. 161–174, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Pöltl, B. Hauer, K. Fuchs, V. Tretter, and W. Sieghart, “Subunit composition and quantitative importance of GABAA receptor subtypes in the cerebellum of mouse and rat,” Journal of Neurochemistry, vol. 87, no. 6, pp. 1444–1455, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. G. Grenningloh, E. Gundelfinger, B. Schmitt et al., “Glycine vs GABA receptors,” Nature, vol. 330, no. 6143, pp. 25–26, 1987. View at Google Scholar · View at Scopus
  15. P. S. Miller and A. R. Aricescu, “Crystal structure of a human GABAA receptor,” Nature, vol. 512, no. 7514, pp. 270–275, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Sieghart and G. Sperk, “Subunit composition, distribution and function of GABA(A) receptor subtypes,” Current Topics in Medicinal Chemistry, vol. 2, no. 8, pp. 795–816, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. P. H. Torkkeli, H. Liu, and A. S. French, “Transcriptome analysis of the central and peripheral nervous systems of the spider Cupiennius salei reveals multiple putative Cys-loop ligand gated ion channel subunits and an acetylcholine binding protein,” PLoS ONE, vol. 10, no. 9, Article ID e0138068, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. C. A. Reid, S. F. Berkovic, and S. Petrou, “Mechanisms of human inherited epilepsies,” Progress in Neurobiology, vol. 87, no. 1, pp. 41–57, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Simon, H. Wakimoto, N. Fujita, M. Lalande, and E. A. Barnard, “Analysis of the set of GABAA receptor genes in the human genome,” The Journal of Biological Chemistry, vol. 279, no. 40, pp. 41422–41435, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. I.-C. Chou, C.-C. Lee, C.-H. Tsai et al., “Association of GABRG2 polymorphisms with idiopathic generalized epilepsy,” Pediatric Neurology, vol. 36, no. 1, pp. 40–44, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Bethmann, J.-M. Fritschy, C. Brandt, and W. Löscher, “Antiepileptic drug resistant rats differ from drug responsive rats in GABAA receptor subunit expression in a model of temporal lobe epilepsy,” Neurobiology of Disease, vol. 31, no. 2, pp. 169–187, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. M. SidAhmed-Mezi, I. Kurcewicz, C. Rose et al., “Mass spectrometric detection and characterization of atypical membrane-bound zinc-sensitive phosphatases modulating GABAA receptors,” PLoS ONE, vol. 9, no. 6, Article ID e100612, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Uwera, S. Nedergaard, and M. Andreasen, “A novel mechanism for the anticonvulsant effect of furosemide in rat hippocampus in vitro,” Brain Research, vol. 1625, pp. 1–8, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. J. L. Fisher, “The anti-convulsant stiripentol acts directly on the GABAA receptor as a positive allosteric modulator,” Neuropharmacology, vol. 56, no. 1, pp. 190–197, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. J.-Q. Kang, W. Shen, C. Zhou, D. Xu, and R. L. Macdonald, “The human epilepsy mutation GABRG2(Q390X) causes chronic subunit accumulation and neurodegeneration,” Nature Neuroscience, vol. 18, no. 7, pp. 988–996, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Hirose, “Mutant GABA(A) receptor subunits in genetic (idiopathic) epilepsy,” Progress in Brain Research, vol. 213, pp. 55–85, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. H. Ding, S.-H. Guo, E.-Z. Deng et al., “Prediction of Golgi-resident protein types by using feature selection technique,” Chemometrics and Intelligent Laboratory Systems, vol. 124, pp. 9–13, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. W.-C. Li, E.-Z. Deng, H. Ding, W. Chen, and H. Lin, “iORI-PseKNC: a predictor for identifying origin of replication with pseudo k-tuple nucleotide composition,” Chemometrics and Intelligent Laboratory Systems, vol. 141, pp. 100–106, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. H. Lin, W. Chen, and H. Ding, “AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes,” PLoS ONE, vol. 8, no. 10, Article ID e75726, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. L.-F. Yuan, C. Ding, S.-H. Guo, H. Ding, W. Chen, and H. Lin, “Prediction of the types of ion channel-targeted conotoxins based on radial basis function network,” Toxicology in Vitro, vol. 27, no. 2, pp. 852–856, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. B. Liu, D. Zhang, R. Xu et al., “Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection,” Bioinformatics, vol. 30, no. 4, pp. 472–479, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Chen, X. Wang, and B. Liu, “IMiRNA-SSF: improving the identification of MicroRNA precursors by combining negative sets with different distributions,” Scientific Reports, vol. 6, Article ID 19062, 2016. View at Publisher · View at Google Scholar · View at Scopus
  33. A. Besga, I. Gonzalez, E. Echeburua et al., “Discrimination between Alzheimer’s disease and late onset bipolar disorder using multivariate analysis,” Frontiers in Aging Neuroscience, vol. 7, article 231, 2015. View at Publisher · View at Google Scholar
  34. Q. Yang, H.-Y. Zou, Y. Zhang et al., “Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm,” Talanta, vol. 147, pp. 609–614, 2016. View at Publisher · View at Google Scholar · View at Scopus
  35. R. Wang, Y. Xu, and B. Liu, “Recombination spot identification Based on gapped k-mers,” Scientific Reports, vol. 6, article 23934, 2016. View at Publisher · View at Google Scholar
  36. A. K. Sharma, S. Kumar, K. Harish, D. B. Dhakan, and V. K. Sharma, “Prediction of peptidoglycan hydrolases—a new class of antibacterial proteins,” BMC Genomics, vol. 17, no. 1, article 411, 2016. View at Publisher · View at Google Scholar
  37. Z. C. Li, M. H. Huang, W. Q. Zhong et al., “Identification of drug-target interaction from interactome network with ‘guilt-by-association’ principle and topology features,” Bioinformatics, vol. 32, no. 7, pp. 1057–1064, 2016. View at Google Scholar
  38. J. J. Jones, B. E. Wilcox, R. W. Benz et al., “A plasma-based protein marker panel for colorectal cancer detection identified by multiplex targeted mass spectrometry,” Clinical Colorectal Cancer, vol. 15, no. 2, pp. 186–194.e13, 2016. View at Publisher · View at Google Scholar
  39. I. Semanjski and S. Gautama, “Smart city mobility application—gradient boosting trees for mobility prediction and analysis based on crowdsourced data,” Sensors, vol. 15, no. 7, pp. 15974–15987, 2015. View at Publisher · View at Google Scholar · View at Scopus
  40. R. Johnson and T. Zhang, “Learning nonlinear functions using regularized greedy forest,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 5, pp. 942–954, 2014. View at Publisher · View at Google Scholar · View at Scopus
  41. K. P. Singh and S. Gupta, “In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches,” Toxicology and Applied Pharmacology, vol. 275, no. 3, pp. 198–212, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. Y. Chen, Z. Jia, D. Mercola, and X. Xie, “A gradient boosting algorithm for survival analysis via direct optimization of concordance index,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 873595, 8 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  43. A. Decruyenaere, P. Decruyenaere, P. Peeters, F. Vermassen, T. Dhaene, and I. Couckuyt, “Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods,” BMC Medical Informatics and Decision Making, vol. 15, article 83, 2015. View at Publisher · View at Google Scholar · View at Scopus
  44. C. Lin, Y. Zou, J. Qin et al., “Hierarchical classification of protein folds using a novel ensemble classifier,” PLoS ONE, vol. 8, no. 2, Article ID e56499, 2013. View at Publisher · View at Google Scholar · View at Scopus
  45. Q. Zou, X. Li, Y. Jiang, Y. Zhao, and G. Wang, “Binmempredict: a web server and software for predicting membrane protein types,” Current Proteomics, vol. 10, no. 1, pp. 2–9, 2013. View at Publisher · View at Google Scholar · View at Scopus
  46. Y. Huang, B. Niu, Y. Gao, L. Fu, and W. Li, “CD-HIT Suite: a web server for clustering and comparing biological sequences,” Bioinformatics, vol. 26, no. 5, pp. 680–682, 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. C. Liu, P. Su, R. Li et al., “Molecular cloning, expression pattern, and molecular evolution of the spleen tyrosine kinase in lamprey, Lampetra japonica,” Development Genes and Evolution, vol. 225, no. 2, pp. 113–120, 2015. View at Publisher · View at Google Scholar · View at Scopus
  48. L. Song, D. Li, X. Zeng, Y. Wu, L. Guo, and Q. Zou, “nDNA-prot: identification of DNA-binding proteins based on unbalanced classification,” BMC Bioinformatics, vol. 15, article 298, 2014. View at Publisher · View at Google Scholar · View at Scopus
  49. Q. Zou, J. Guo, Y. Ju, M. Wu, X. Zeng, and Z. Hong, “Improving tRNAscan-SE annotation results via ensemble classifiers,” Molecular Informatics, vol. 34, no. 11-12, pp. 761–770, 2015. View at Publisher · View at Google Scholar · View at Scopus
  50. C. Lin, W. Chen, C. Qiu, Y. Wu, S. Krishnan, and Q. Zou, “LibD3C: ensemble classifiers with a clustering and dynamic selection strategy,” Neurocomputing, vol. 123, pp. 424–435, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. Q. Zou, Z. Wang, X. Guan, B. Liu, Y. Wu, and Z. Lin, “An approach for identifying cytokines based on a novel ensemble classifier,” BioMed Research International, vol. 2013, Article ID 686090, 11 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  52. L. Wei, M. Liao, Y. Gao, R. Ji, Z. He, and Q. Zou, “Improved and promising identification of human microRNAs by incorporatinga high-quality negative set,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. 1, pp. 192–201, 2014. View at Publisher · View at Google Scholar · View at Scopus
  53. X. Zeng, S. Yuan, X. Huang, and Q. Zou, “Identification of cytokine via an improved genetic algorithm,” Frontiers of Computer Science, vol. 9, no. 4, pp. 643–651, 2015. View at Publisher · View at Google Scholar · View at Scopus
  54. Q. Zou, J. Zeng, L. Cao, and R. Ji, “A novel features ranking metric with application to scalable visual and bioinformatics data classification,” Neurocomputing, vol. 173, pp. 346–354, 2016. View at Publisher · View at Google Scholar · View at Scopus
  55. H. Ding, Z. Y. Liang, F. B. Guo, J. Huang, W. Chen, and H. Lin, “Predicting bacteriophage proteins located in host cell with feature selection technique,” Computers in Biology and Medicine, vol. 71, pp. 156–161, 2016. View at Publisher · View at Google Scholar
  56. H. Tang, W. Chen, and H. Lin, “Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique,” Molecular BioSystems, vol. 12, no. 4, pp. 1269–1275, 2016. View at Publisher · View at Google Scholar
  57. L. Wei, Q. Zou, M. Liao, H. Lu, and Y. Zhao, “A novel machine learning method for cytokine-receptor interaction prediction,” Combinatorial Chemistry & High Throughput Screening, vol. 19, no. 2, pp. 144–152, 2016. View at Publisher · View at Google Scholar
  58. E. Frank, M. Hall, L. Trigg, G. Holmes, and I. H. Witten, “Data mining in bioinformatics using Weka,” Bioinformatics, vol. 20, no. 15, pp. 2479–2481, 2004. View at Publisher · View at Google Scholar · View at Scopus
  59. T. C. Smith and E. Frank, “Introducing machine learning concepts with WEKA,” in Statistical Genomics, E. Mathé and S. Davis, Eds., vol. 1418 of Methods in Molecular Biology, pp. 353–378, Springer, Berlin, Germany, 2016. View at Publisher · View at Google Scholar
  60. T. L. Bailey, J. Johnson, C. E. Grant, and W. S. Noble, “The MEME Suite,” Nucleic Acids Research, vol. 43, no. W1, pp. W39–W49, 2015. View at Publisher · View at Google Scholar
  61. K.-C. Chou, “Prediction of protein cellular attributes using pseudo-amino acid composition,” Proteins, vol. 43, no. 3, pp. 246–255, 2001. View at Publisher · View at Google Scholar · View at Scopus
  62. H.-B. Shen and K.-C. Chou, “PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition,” Analytical Biochemistry, vol. 373, no. 2, pp. 386–388, 2008. View at Publisher · View at Google Scholar · View at Scopus
  63. B. Liu, F. Liu, X. Wang, J. Chen, L. Fang, and K. Chou, “Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences,” Nucleic Acids Research, vol. 43, no. W1, pp. W65–W71, 2015. View at Publisher · View at Google Scholar
  64. N. Xiao, D.-S. Cao, M.-F. Zhu, and Q.-S. Xu, “Protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences,” Bioinformatics, vol. 31, no. 11, pp. 1857–1859, 2015. View at Publisher · View at Google Scholar · View at Scopus
  65. W. Chen, P.-M. Feng, E.-Z. Deng, H. Lin, and K.-C. Chou, “iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition,” Analytical Biochemistry, vol. 462, pp. 76–83, 2014. View at Publisher · View at Google Scholar · View at Scopus
  66. B. Liu, F. L. Liu, L. Y. Fang, X. L. Wang, and K.-C. Chou, “repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects,” Bioinformatics, vol. 31, no. 8, pp. 1307–1309, 2015. View at Publisher · View at Google Scholar · View at Scopus
  67. L. Au and D. F. Green, “Direct calculation of protein fitness landscapes through computational protein design,” Biophysical Journal, vol. 110, no. 1, pp. 75–84, 2016. View at Publisher · View at Google Scholar · View at Scopus
  68. J. T. S. Hopper and C. V. Robinson, “Mass spectrometry quantifies protein interactions-from molecular chaperones to membrane porins,” Angewandte Chemie—International Edition, vol. 53, no. 51, pp. 14002–14215, 2014. View at Publisher · View at Google Scholar · View at Scopus
  69. K. Kržišnik and T. Urbic, “Amino acid correlation functions in protein structures,” Acta Chimica Slovenica, vol. 62, no. 3, pp. 574–581, 2015. View at Publisher · View at Google Scholar · View at Scopus
  70. A. Olivera-Nappa, B. A. Andrews, and J. A. Asenjo, “Mutagenesis Objective Search and Selection Tool (MOSST): an algorithm to predict structure-function related mutations in proteins,” BMC Bioinformatics, vol. 12, article 122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  71. C. B. Pinheiro, M. Shah, E. L. Soares et al., “Proteome analysis of plastids from developing seeds of Jatropha curcas L.,” Journal of Proteome Research, vol. 12, no. 11, pp. 5137–5145, 2013. View at Publisher · View at Google Scholar · View at Scopus
  72. C. Z. Cai, L. Y. Han, Z. L. Ji, X. Chen, and Y. Z. Chen, “SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence,” Nucleic Acids Research, vol. 31, no. 13, pp. 3692–3697, 2003. View at Publisher · View at Google Scholar · View at Scopus
  73. J. R. Glausier and D. A. Lewis, “Selective pyramidal cell reduction of GABA(A) receptor α1 subunit messenger RNA expression in schizophrenia,” Neuropsychopharmacology, vol. 36, no. 10, pp. 2103–2110, 2011. View at Publisher · View at Google Scholar · View at Scopus
  74. N. Onori, C. Turchi, G. Solito, R. Gesuita, L. Buscemi, and A. Tagliabracci, “GABRA2 and alcohol use disorders: no evidence of an association in an Italian case-control study,” Alcoholism: Clinical and Experimental Research, vol. 34, no. 4, pp. 659–668, 2010. View at Publisher · View at Google Scholar · View at Scopus
  75. H. Yuan, C.-M. Low, O. A. Moody, A. Jenkins, and S. F. Traynelis, “Ionotropic GABA and glutamate receptor mutations and human neurologic diseases,” Molecular Pharmacology, vol. 88, no. 1, pp. 203–217, 2015. View at Publisher · View at Google Scholar · View at Scopus
  76. J. Richetto, M. A. Labouesse, M. M. Poe et al., “Behavioral effects of the benzodiazepine-positive allosteric modulator SH-053-2′F-S-CH3 in an immune-mediated neurodevelopmental disruption model,” The International Journal of Neuropsychopharmacology, vol. 18, no. 4, pp. 1–11, 2014. View at Publisher · View at Google Scholar · View at Scopus
  77. R. J. Hatch, C. A. Reid, and S. Petrou, “Enhanced in vitro CA1 network activity in a sodium channel β1(C121W) subunit model of genetic epilepsy,” Epilepsia, vol. 55, no. 4, pp. 601–608, 2014. View at Publisher · View at Google Scholar · View at Scopus
  78. R. Kumari, R. Lakhan, J. Kalita, R. K. Garg, U. K. Misra, and B. Mittal, “Potential role of GABAA receptor subunit; GABRA6, GABRB2 and GABRR2 gene polymorphisms in epilepsy susceptibility and pharmacotherapy in North Indian population,” Clinica Chimica Acta, vol. 412, no. 13-14, pp. 1244–1248, 2011. View at Publisher · View at Google Scholar · View at Scopus
  79. Y. L. Murashima and M. Yoshii, “New therapeutic approaches for epilepsies, focusing on reorganization of the GABAA receptor subunits by neurosteroids,” Epilepsia, vol. 51, no. 3, pp. 131–134, 2010. View at Publisher · View at Google Scholar · View at Scopus
  80. C. Chiron, “Current therapeutic procedures in Dravet syndrome,” Developmental Medicine and Child Neurology, vol. 53, supplement 2, pp. 16–18, 2011. View at Publisher · View at Google Scholar · View at Scopus
  81. G. Gallos, P. Yim, S. Chang et al., “Targeting the restricted α-subunit repertoire of airway smooth muscle GABAA receptors augments airway smooth muscle relaxation,” American Journal of Physiology—Lung Cellular and Molecular Physiology, vol. 302, no. 2, pp. L248–L256, 2012. View at Publisher · View at Google Scholar · View at Scopus
  82. G. M. Sizemore, S. T. Sizemore, D. D. Seachrist, and R. A. Keri, “GABA(A) receptor Pi (GABRP) stimulates basal-like breast cancer cell migration through activation of extracellular-regulated kinase 1/2 (ERK1/2),” Journal of Biological Chemistry, vol. 289, no. 35, pp. 24102–24113, 2014. View at Publisher · View at Google Scholar · View at Scopus
  83. L. I. Sinclair, P. T. Dineen, and A. L. Malizia, “Modulation of ion channels in clinical psychopharmacology: adults and younger people,” Expert Review of Clinical Pharmacology, vol. 3, no. 3, pp. 397–416, 2010. View at Publisher · View at Google Scholar · View at Scopus
  84. A. S. Al Mansouri, D. E. Lorke, S. M. Nurulain et al., “Methylene blue inhibits the function of α7-nicotinic acetylcholine receptors,” CNS and Neurological Disorders—Drug Targets, vol. 11, no. 6, pp. 791–800, 2012. View at Publisher · View at Google Scholar · View at Scopus
  85. J. Lu, Q. Zhang, D. Tan et al., “GABA A receptor π subunit promotes apoptosis of HTR-8/SVneo trophoblastic cells: implications in preeclampsia,” International Journal of Molecular Medicine, vol. 38, no. 1, pp. 105–112, 2016. View at Publisher · View at Google Scholar
  86. A. P. Hanek, H. A. Lester, and D. A. Dougherty, “Photochemical proteolysis of an unstructured linker of the GABAAR extracellular domain prevents GABA but not pentobarbital activation,” Molecular Pharmacology, vol. 78, no. 1, pp. 29–35, 2010. View at Publisher · View at Google Scholar · View at Scopus