IN SILICO ANALYSIS RELATED TO TIGR4 STRAIN IN STREPTOCOCCUS PNEUMONIAE

Authors

  • Balasankar Karavadi Department of Bioinformatics, School of Bio-Chemical Engineering, Sathyabama University, Chennai – 600 119, Tamil Nadu, India.
  • Pooja Suresh Department of Bioinformatics, School of Bio-Chemical Engineering, Sathyabama University, Chennai – 600 119, Tamil Nadu, India.

DOI:

https://doi.org/10.22159/ajpcr.2018.v11i4.23731

Keywords:

Docking, ADMET, Modeler, Receptor, TIGR4

Abstract

 Objective: Numerous current investigations are done on the efficiency of natural components to combat the invasion by Streptococcus pneumoniae – strain TIGR4; the main objective is to propose the most favorable ligand compound that could be effective to target the protein.

Methods: The normal segments from the Melissa officinalis are docked against serine/threonine protein kinase (STPK) receptor. The tools and programming utilized are modeler v 9.10 for displaying the protein structure, PubChem compound database to recover the synthetic structure of the ligands. ADMET was used to know the toxicity of the ligands and data warrior and the docking analysis was done by PyRx.

Result: The results show that 5-cedranone compounds satisfy the ADMET properties and are more favorable to bind with STPK receptor. The drug score of 5-cedranone is 0.4572 and the m binding energy is −7.9.

Conclusions: The amino acid residue for the least binding energy for STPK is Ser 175 and Thr 167. Based on the ADMET analysis, 5-cedranone shows moderate cLogP and cLogS values and we predict 5-cedranone may not produce any side effects.

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References

Plotkin SA, Orenstein W, Offit PA. Vaccines. Philadelphia, PA: Elsevier – Saunders; 2012. p. 542-51.

Wainer H. Medical Illuminations; Using Evidence, Visualization and Statistical Thinking to Improve Healthcare. Oxford: Oxford University Press; 2014. p. 53-4.

Griffith F. The Significance of Pneumococcal Types. J Hyg 1928;27:113-59.

Karavadi B, Suresh MX. Homology modeling and molecular drug design approach in identifying drug targets of TIGR4 in Streptococcus pneumonia. Biosci Biotechnol Res Asia 2014;11:517-22.

Karavadi B, Suresh XM. Homology modeling of polymerase and CPS biosynthesis proteins in CGSP14 strain of Streptococcus pneumonia and its ligand identification: An in silico approach. Asian J Pharm Clin Res 2014;7:162-5.

Karavadi B, Suresh MX. In silico modeling of capsular polysaccharide biosynthesis protein and tyrosine kinase of G54 strain in Streptococcus pneumoniae and their ligand identification. Int J Pharm Pharm Sci 2014;6:547-50.

Karavadi B, Suresh MX. Receptor identification and lead molecular discovery of phage encoded protein in TCH8431/19A strain of Streptococcus pneumoniae: A computational approach. Int J Appl Pharm 2014;6:6-10.

Pundir S, Martin MJ, O’Donovan C, UniProt Consortium. UniProt tools. Curr Protoc Bioinformatics 2016;53:1.29.1-15.

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215:403-10.

Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A. PubChem substance and compound databases. Nucleic Acids Res 2016;44:1202-13.

Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. Drug bank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 2006;34:668-72.

Spill YG, Kim SJ, Schneidman-Duhovny D, Russel D, Webb B, Sali A, et al. SAXS merge: An automated statistical method to merge SAXS profiles using Gaussian processes. J Synchrotron Radiat 2014;21:203-8.

Ramachandran GN, Ramakrishnan C, Sasisekharan V. Stereochemistry of polypeptide chain configurations. J Mol Biol 1963;7:95-9.

Published

01-04-2018

How to Cite

Karavadi, B., and P. Suresh. “IN SILICO ANALYSIS RELATED TO TIGR4 STRAIN IN STREPTOCOCCUS PNEUMONIAE”. Asian Journal of Pharmaceutical and Clinical Research, vol. 11, no. 4, Apr. 2018, pp. 96-99, doi:10.22159/ajpcr.2018.v11i4.23731.

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