Drug reposition of Amoxycillin by molecular docking

Introduction

The completion of the human genome project has resulted in an increasing number of new therapeutic targets for drug discovery. High-throughput protein purification, crystallography and nuclear magnetic resonance spectroscopy techniques have been developed and contributed to many structural details of proteins and protein-ligand complexes [1[. These advances allow the computational strategies to permeate all aspects of drug discovery today. As to structure-based drug design, molecular docking is the most common method which has been widely used ever since the early 1980s [2] . Programs based on different algorithms were developed to perform molecular docking studies, which have made docking an increasingly important tool in pharmaceutical research [3] . The molecular docking approach can be used to model the interaction between a small molecule and a protein at the atomic level, which allow us to characterize the behavior of small molecules in the binding site of target proteins as well as to elucidate fundamental biochemical processes [4] . The docking process involves two basic steps: prediction of the ligand conformation as well as its position and orientation within these sites (usually referred to as pose) and assessment of the binding affinity [5] . Knowing the location of the binding site before docking processes significantly increases the docking efficiency. In many cases, the binding site is indeed known before docking ligands into it. Also, one can obtain information about the sites by comparison of the target protein with a family of proteins sharing a similar function or with proteins co-crystallized with other ligands [6] . Docking has been performed with a flexible ligand and a rigid receptor for a long time, and remains the most popular method in use [7] . Molecular docking has been the most widely employed technique. Though the main application lies in structure-based virtual screening for identification of new active compounds towards a particular target protein, in which it has produced a number of success stories [8] . Generally, the drug development is a time consuming, over priced process with a lower success rate. To overcome these difficulties, multiple computational approaches including drug repositioning are used in recent days. 9 Drug repositioning minimizes the cost and time in the drug development process due to the known efficacy and therapeutic potential against other ailments. Development of new broad spectrum antibiotics is increasingly difficult and thus it is essential to utilize such therapeutic alternatives to combat the infections. This involves the investigation of existing drugs for new therapeutic purposes [10, 11] . Amoxicillin is an antibiotic often used for the treatment of a number of bacterial infections such as skin infection, pneumonia and urinary tract infections. The routes of administration include oral or less commonly intravenous [12] .

International Journal of Advanced Chemistry Research

The Target protein is Penicillin-binding protein 1A with a general function of transferase activity and transferring glycosyl groups. The specific functions include cell wall formation, synthesis of cross-linked peptidoglycan from the lipid intermediates. The enzyme has a penicillin-insensitive transglycosylase N-terminal domain. Pharmacogenomics attempts to eliminate the trial-and-error method of prescribing, allowing physicians to take into consideration their patient's genes, the functionality of these genes, and how this may affect the efficacy of the patient's current or future treatments.

To screen FDA drugs for shape based similarity with Amoxicillin using drug repossession approach and screening the drugs for possible therapeutic effects other than their approved activity to generate drug-gene interaction networks for better pharmacogenomics understanding [13] .

Materials and Methods

The Workflow used for Drug Repositioning Method is shown in Figure 2.

Retrieval of Protein Structure

The protein structure of penicilin binding protein 1A was studied (PDBID:2ZC5).

Shape Based Screening

The smile format and chemical structure of amoxicillin was retrieved from FDA and Swiss Similarity online platform was used to identify some chemical hits from FDA and other libraries with respect to the reference structure of amoxicillin. Amoxicillin was used as standard template to screen FDA approved drugs.

Molecular Docking

The aim of molecular docking is to give a prediction of the ligand-receptor complex structure using computation methods. Docking can be achieved through two interrelated steps: first by sampling conformations of the ligand in the active site of the protein; then ranking these conformations via a scoring function.

Ideally, sampling algorithms should be able to reproduce the experimental binding mode and the scoring function should also rank it highest among all generated conformations. The structure of the screened drugs was obtained from PubChem in their SDF (Structures Data File) file format. Docking was performed on all the screened drugs against the penicillin binding protein 1A. The binding pocket of the target protein was confirmed from CASTp online tool. In docking experiments the grid box dimensions were adjusted as X=19.12, Y=20.44, Z=17.10 respectively by default exhaustiveness=8 value. Top ten screened drugs were selected based on the best binding energy.

Fig 6: Grid Box

Results and Discussion

Based on PyRx docking results, the top ten drugs with best binding affinity was selected. The best binding energy (-5.4kcal/mol) was obtained and it was studied that how well different drug molecules bind to the same protein.

Conclusion

Molecular docking has been widely used for discovery of drugs. The present study evaluates the repositioning of amoxicillin for added therapeutic activities. Out of the FDA approved drugs, the best ten drugs with structural similarity were displayed. The repositioned drug interaction was different from the original interactions reported with the respective genes. The interaction of the drug with various genes was studied and the network model was built to know the properties of the drug gene interaction. Further, to improve the study, molecular dynamics simulation using Gromacs and Designing of pharmacogenomics network model using Drug gene interaction database and Drug signatures database would throw light on the unseen portions of the study.

References

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