Molecular docking analysis of timepidium with Acetylcholine and lumacaftor with GABA(A) activator

Epilepsy is a chronic disorder characterized by disturbed tissue related molecular activity within the brain irrespective of age. The cause is very difficult to understand towards a suitable treatment. However, its symptoms like seizures are treated and suppressed by known medications. Moreover, the condition is linked with neuro-transmitters such as GABA (gamma amino butyric acid) and acetylcholine. Therefore, it is of interest to design and develop inhibitors for these targets. Hence, we describe the molecular binding features of timepidium with acetylcholine and lumacaftor with GABA(A) activator using molecular docking based geometric optimization and screening analysis for further consideration.

GABA (A) receptor only [9]. Herbal compounds are also known to activate GABA receptor such as Rosmarinic Acid and Kaempferol [10].
The treatment of epilepsy after occurrence of first seizure is a controversial issue because the underlying mechanisms of brain damage and processes that lead to the development of epileptic conditions are still unknown. However, many successful antiepileptic drugs AED's have been developed to control seizures; which is one of the most common conditions of epilepsy. These drugs mainly include brivaracetam [11], topiramate [12], phensuximide [13] and fingolimod [14]. AED's stop seizures in approximately 70% of people by controlling chemical activity in brain but they do not cure epilepsy. A study was conducted to check the drug resistance in epileptic patients. If drugs are not effective then seizure activity may be treated either by ketogenic diet [15] or by surgery [16]. It investigated the use of complementary and alternative medicine (CAM) among epileptic patients. It also analyzed the impact of CAM on AED's. The results

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©Biomedical Informatics (2019) showed that there is less association between AED's and use of CAM [17].

Methodology: Protein target and ligand structures:
The first step was extraction of three-dimensional structures of drugs and proteins. The 3D structures of GABA activators and the program database files (PDB) of Acetylcholine inhibitors were downloaded from Drug Bank Database for docking. The protein ID's for the chosen proteins were obtained from Uniprot. These IDs were then used as an input to download PDB structure of Acetylcholine and GABA receptors from protein data bank. Different receptor chains of Acetylcholine and GABA were analyzed. However, on the basis of their functional properties, six receptor chains of Acetylcholine and four chains of GABA were selected. 45 drugs were randomly selected for Acetylcholine while 47 were selected for GABA. Acetylcholine and GABA recognized some of these drugs while others were unrecognized.

Electrostatic interactions calculation:
The next step was calculation of electrostatic interactions. SCORE, is used to calculate the electrostatic interactions between the protein as receptor/target and drug. These electrostatic interactions were calculated between randomly selected recognized and unrecognized drugs and target proteins i.e., extracted protein chains of Acetylcholine and GABA. For Acetylcholine, one drug is interacted with three chains (out of six chains) whereas in case of GABA three drugs are interacted with three chains (out of four chains). These interactions are shown in Table 1.

Molecular docking analysis:
Selecting the highly negative interactions between receptor and ligand using publically available Docking Server and Hex software performed the molecular docking. Chimera was used to visualize results of docking between protein chains and drugs. When drug binds to its target, it releases binding free energy. The binding free energies of the ligand and proteins were computed by using the compute energy tool of the Swiss PDB viewer. The docking server was then used to validate the post docking results. Motifs and domains of the receptor protein were then obtained using SCANPROSITE and ProDom. Examining motifs and domains of the considered protein then did a comparison of the active sites. The residues that lie between the sequence of the motifs and chains were considered as best docked results.

Results and Discussion:
The results of scoring show positive and negative electrostatic interactions between drugs and their targets. The negative electrostatic interaction indicates more possibility of drug binding with protein. Table 2 indicates that 21 drugs, which are not recognized for Acetylcholine, interacted with all chains of Acetylcholine. It was found that out of these 21 drugs, the bestinteracted drug is Timepidium, which gives highly negative electrostatic interaction. However, in case of GABA, 27 drugs that were not recognized by GABA interacted with the chains of GABA. The results of docking are shown in Figure 1. In a number of docking results, Timepidium has produced the best result of docking with Acetylcholine receptor 5FJV instead of 6CN. Figure  2a shows that there is only one hydrogen bond between Glutamine residue of Chain A of 5FJV and Oxygen atom of the drug. The results of docking indicate maximum hydrogen and polar bonds between acetylcholine chain with Timepidium and GABA with Lumacaftor. The hydrogen and polar bonds formed between the receptor-ligand complexes of Acetycholine receptor and Timepidium are shown in Table 3. The different types of bond linkages indicate the best-docked results of GABA receptor and Lumacaftor. Table 3 also gives Hydrogen and polar bonds between the oxygen atom of Lumacaftor and the GABA chains. Thus, the presence of hydrogen and polar bonds validate the acquired results. Table 4 is showing binding free energies of drug and their targets. The negative value of the binding energy of the proteinligand complex is preferred for binding of ligand with its desired protein. It was observed that 4MQE gave highly negative binding free energy with Lysine A whereas 5FJV showed negative interaction with Glutamine B. Table 5 indicates the predicted binding sites of the chains of the studied proteins. Comparing predicted binding sites of their Motifs and Domains then checked the presence of active sites in Acetylcholine and GABA receptor binding sites. The results indicate that all the binding sites of Acetylcholine receptor chain (5FJV) and GABA receptor chain (4MQE) are present in their corresponding Domains. However, in other chains of GABA and Acetylcholine, the binding sites were not matched in motifs and domains.

Conclusion:
Epilepsy is known to be linked with neuro-transmitters such as GABA (gamma amino butyric acid) and acetylcholine. Therefore, it ©Biomedical Informatics (2019) is of interest to design and develop inhibitors for these targets. However, it is known that lumacaftor has been used to treat Cystic fibrosis (CF) [19] and timepidium bromide to treat abdominal diseases [20]. Hence, it is of importance to evaluate and describe the molecular binding features of timepidium with acetylcholine and lumacaftor with the GABA(A) activator using molecular docking based geometric optimization and screening analysis for further consideration in this context.