GABAA receptor binding molecules from Traditional Chinese Medicine: An in silico approach

The advents of how anesthesia works have helped in the discovery of anesthetic target protein. One such target protein named Υ-aminobutyric acid (GABAA), which is the chief inhibitory neurotransmitter in the mammalian central nervous system. Asparagine at 289 position of GABAA protein within TM2 is important for its anesthetic function. This study explores Traditional Chinese Medicine (TCM) against ASN 289 of GABAA for novel anesthetic compounds. The in silico approach showed gastrodin out of all compounds to be the best compound to start further analysis. It is a potential anesthetic compound suitable for the development of new drug. Article Info Received: 30 July 2014 Accepted: 25 August 2014 Available Online: 1 October 2014 DOI: 10.3329/bjp.v9i4.19731 Lv M, Wang XT, Li K, Wang YL. GABAA receptor binding molecules from Traditional Chinese Medicine: An in silico approach. Bangladesh J Pharmacol. 2017; 12: 447-51. This work is licensed under a Creative Commons Attribution 3.0 License. You are free to copy, distribute and perform the work. You must attribute the work in the manner specified by the author or licensor. GABAA receptor binding molecules from Traditional Chinese Medicine: An in silico approach Meng Lv1,2, Xue Tao Wang2, Kun Li3,4, and Yue Lan Wang1 Department of Anesthesiology, Qianfoshan Hospital, Shandong University, Shandong 250014,China; Department of Anesthesiology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Shandong 250014, China; Department of Gynaecology and Obstetrics, Affiliated Hospital of Shandong Academy of Medical Science, Shandong 250031, China; Department of Gynaecology and Obstetrics, Shandong Academy of Medical Sciences, Shandong 250031, China.


Introduction
The molecular mechanism of general anesthesia is a classical unsolved problem of neuro pharmacology (Miller, 1985), but the advents in the past three decades have shed some insight into how anesthetic drug might work (Urban, 2008). Since general anesthesia discovery, hundreds of compounds have been identified as anesthetic drugs (Urban et al., 2006), out of those only few have been introduced into clinical practice (Hardman et al., 2001). Their mechanism is an arguable aspect and a plethora of work has been done to understand there working (Hopkins et al., 2002;Campagna et al., 2003;Sonner et al., 2003). These works have pointed out ligand-gated ion channels as potential targets for general anesthetics and ϒ-aminobutyric acid (GABAA) is the most studied among them.
Etomidate, propofol, barbiturates, isoflurane, and sevoflurane are the known compounds that significantly increase the activity of GABAA receptors at clinically relevant concentrations (Krasowski and Harrison, 1999;Yamakura and Harris, 2000). The intravenous anesthe-tics etomidate and propofol, as well as pentobarbital, have been shown to exert their immobilizing action and in part their hypnotic action through β3-containing GABAA receptors anesthetic action (Zellar et al., 2008). The action of the etomidate at type A GABA receptors has been shown to be highly dependent upon a single amino acid (ASN-289) residue within TM2 of the βsubunit (Belelli et al., 1997).
In silico approach can be used to discover new, potent anesthetic drug (Eckenhoff et al., 2008). In this study modern computational tools are used to design and analysis some Traditional Chinese Medicine (TCM) against the proposed anesthetic binding site of GABAA , the results generated are then compared with that of etomidate, to give us an idea about the effectiveness of the proposed drug before going for further in vitro and in vivo analysis.
The GABAA protein sequence was retrieved from Uniprot (http://www.uniprot.org/). The protein contains 512 amino acids and molecular weight of 59,150 Daltons. The GABAA 3D structure models were generated by I-TASSER software by Zhang. I-TASSER (Zhang, 2008). The structure validation was done in SAVES server by PROCHECK (Laskowski et al., 1993).

TCM selection
In house TCM database was used for the study, the database constitutes around 1,400 com-pounds of natural origin. The compounds were screened against the active site of GABAA protein using AutoDock vina (Vina, 2010).

ADME/T and drug likeliness prediction
The top hundred compounds were checked for their pharmacokinetic properties using online server named pre-ADMET. Only compounds strictly following absorption, distribution, metabolism and elimination are selected for further analysis.

Molecular docking analysis
Molecular docking analysis of the GABAA with the top five TCM compounds was done using AutoDock 4.2 (Morris et al., 2009). The tool uses binding free energy evaluation to find the best binding mode between the compound and the protein, the energy values are calculated by the characterization of intermolecular energy, internal energy of ligand, and torsional free energy. All the visualizations were generated by Pymol (Pymol, 2009).

Result and Discussion
The 3D structure of the protein was modeled using I-TASSER, an online server which uses threading method. The models proposed by the server have good quality and resolution, out of the five generated models, the one with the best C-score shown in Figure  1a was selected for the study. The C-score is a measure to observe the quality of generated models. The best modeled structure of GABAA was subjected to PROCHECK in SAVES server the result depicting the psi-phi distribution is shown in Figure 1b, the amino acids arranged in most favored regions in plot were 88.8% and some of the amino acids in disallowed regions of 3.2%.
The generated structure was used virtual screening, where the in house database of TCM was screened against ASN289 using AutoDock vina. Top hundred compounds were selected for further drug likeness and ADME/tox analysis. The results generated were used to shorten the number of compounds for further molecular docking study, only five compounds fulfilled the criteria of drug likeness and Lipinski rule of five (Lipinski, 2004). Table I and II shows the results generated for the top five compounds. The five compounds shown in Figure 2 are all Chinese traditional medicine, used for different diseases. Hydrogen bond interaction provide a very important contribution to the binding affinity for ligands. Three of the five selected compounds are showing hydrogen bond interaction with GABAA. Table III shows the results generated by AutoDock 4.2. All the interactions

Conclusion
Out of 1400 compounds, five were shortlisted based on drug likeness and binding energy. Out of five selected compounds only three compounds showed hydrogen bond interactions with GABAA. Gastrodin out of all compounds is the best compound to start further analysis. Its generated result suggests that it is a potential anesthetic compound suitable for the development of new drug.

Financial Support
Natural