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Biochemical and Structural Characterization of Helicobacter pylori Peptide deformylase by Computational Methods

Surekha Patil, Shivakumar B. Madagi

Abstract


Helicobacter pylorus is classified as class one carcinogen in causing gastric cancer. Peptide deformylases (PDFs) present in bacteria removes the formyl group from the N-terminal Methionine of newly synthesized proteins which is necessary in protein maturation. These enzymes have shown as new targets in antibacterial, antimalarial and anticancer drug discovery. Here an insilico technique was initiated to characterize structural and biochemical properties of this protein. Pair wise sequence alignment with human PDF shows 31–47% identity, hence can be used as anticancer target in human. This enzyme is located in the cytoplasm of the cell, do not contain signal peptide, promiscuity site and vaccine targets. Physicochemical characterization is analyzed by Protparam tool reveals that this is an unstable protein, predicted Iso Electric Ph is 6.9 with total positively charged residues 29 and negatively charged residues 24. Conserved domain search shows one conserved domain and interacting partner from STRING search shows 10 interacting proteins. PDF is searched for MHC class-1 binding sites with 12 super types of MHC class-1 molecules has highest binding site of 8 and least binding site 1. Understanding of these characteristics of H. pylori peptide de-formulas may help in designing novel drugs to cure H. pylori caused infections.

 

Keywords: H. pylori, Peptide deformylases, computational tools, sequence alignment, conserved, Domain, epitopes

 

Cite this Article

Surekha Patil, Shivakumar B Madagi. Biochemical and Structural Characterization of Helicobacter pylori Peptide Deformylase by Computational Methods. Research & Reviews: A Journal of Bioinformatics. 2017; 4(3): 22–28p.


Keywords


H.pylori, Peptide deformylase, computational tools, sequence alignment, conserved domain, epitopes

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References


Conteduca, V., Sansonno, D., Lauletta, G., Russi, S., Ingravallo, G., & Dammacco, F. (2013). H. pylori infection and gastric cancer: state of the art. International journal of oncology, 42(1), 5-18.

IARC Working Group. (1994). Schistosomes, liver flukes and Helicobacter pylori. IARC working group on the evaluation of carcinogenic risks to humans. Lyon, 7-14 June 1994. IARC Monogr Eval Carcinog Risks Hum., 61, 1-241.

Abadi, A. T. B. (2016). Helicobacter pylori and gastric cancer. Frontiers in medicine, 3.

Talebi Bezmin Abadi, A. (2014). Therapy of Helicobacter pylori: present medley and future prospective. BioMed research international, 2014.

Chey, W. D., & Wong, B. C. (2007). American College of Gastroenterology guideline on the management of Helicobacter pylori infection. The American journal of gastroenterology, 102(8), 1808-1825.

Jeon, J., Nim, S., Teyra, J., Datti, A., Wrana, J. L., Sidhu, S. S., & Kim, P. M. (2014). A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome medicine, 6(7), 57.

Apfel, C. M., Locher, H., Evers, S., Takács, B., Hubschwerlen, C., Pirson, W., & Keck, W. (2001). Peptide deformylase as an antibacterial drug target: target validation and resistance development. Antimicrobial agents and chemotherapy, 45(4), 1058-1064.

N Sangshetti, J., Kalam Khan, F. A., & B Shinde, D. (2015). Peptide deformylase: a new target in antibacterial, antimalarial and anticancer drug discovery. Current medicinal chemistry, 22(2), 214-236.

Nair, R., & Rost, B. (2005). Mimicking cellular sorting improves prediction of subcellular localization. Journal of molecular biology, 348(1), 85-100.

Shen, H. B., & Chou, K. C. (2010). Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins. Journal of theoretical biology, 264(2), 326-333.

Yu, C. S., Lin, C. J., & Hwang, J. K. (2004). Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions. Protein science, 13(5), 1402-1406.

Gardy, J. L., Laird, M. R., Chen, F., Rey, S., Walsh, C. J., Ester, M., & Brinkman, F. S. (2004). PSORTb v. 2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics, 21(5), 617-623.

Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S. E., Wilkins, M. R., Appel, R. D., & Bairoch, A. (2005). Protein identification and analysis tools on the ExPASy server (pp. 571-607). Humana Press.

Marchler-Bauer, A., Derbyshire, M. K., Gonzales, N. R., Lu, S., Chitsaz, F., Geer, L. Y., ... & Lanczycki, C. J. (2014). CDD: NCBI's conserved domain database. Nucleic acids research, 43(D1), D222-D226.

Käll, L., Krogh, A., & Sonnhammer, E. L. (2004). A combined transmembrane topology and signal peptide prediction method. Journal of molecular biology, 338(5), 1027-1036.

Linding, R., Jensen, L. J., Diella, F., Bork, P., Gibson, T. J., & Russell, R. B. (2003). Protein disorder prediction: implications for structural proteomics. Structure, 11(11), 1453-1459.


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