Proteomic Approach of Medicinal Plant and Its Informatics Use in Health Science

  • Soumya Khare Department of Biotechnology, Kalyan PG College, Bhilai Nagar, Chhattisgarh
  • Tanushree Chatterjee Department of Biotechnology, Raipur Institute of Technology, Raipur Chhattisgarh
  • Amiy Dutt Chaturvedi Plant Ecology and Environmental Science Divisions CSIR- National Botanical Research Institute, Lucknow, Uttar Pradesh
Keywords: KEGG (Kyoto Encyclopedia of Genes and Genomes), USDA (United States Department of Agriculture), TCM- ID (Traditional Chinese Medicine Information Data).

Abstract

Medical informatics has recently been a part of bioinformatics, the clinical proteomic plays an important role in the analysis of genetic. Proteomic is set to play a major role in defining biological systems at a molecular level. Medicinal plants are used as a raw material for some important drugs and antibiotics brought about a revolution in controlling different disease. The potential of higher plants as source for new drugs is still largely unexplored. Proteomic provides essential mechanisms to analyze information generated from using databases techniques. In particular, these approaches have made possible for the identification of genes and pathways involved in synthesis of bioactive metabolites in medicinal plants. Here we look at large scale proteomic in the post genomic era and reflect on its future impact to study biological systems in health science and medical research. Its main aim is to convert raw gene sequences data and measurement of gene expression, to informations describing the actions of those proteins controlling biological systems. This paper reviews on the bioinformatics have deal with to medicinal plants research and highlight a crucial role in working towards health science.

References

1. Blackstock WP, Weir MP. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol 1999;17:121-127.
2. Jorgensen WL. The many roles of computation in drug discovery. Science 2004;303:1813-1818.
3. Saito K, Matsuda F. Metabolomics for functional genomics, systems biology and biotechnology. Annu Rev Plant Biol 2010;61:463-489.
4. Kavallaris M., Marshall GM. Proteomics and disease: opportunities and challenges. MJA 2005;182:575-579.
5. Sayers EW, Barrett T, Benson DA, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2010;38:5-16.
6. Zhu, H., J.F. Klemic, S. Chang, et al. Analysis of yeast protein kinases using protein chips. Nat. Genet.2001;26:283-289.
7. Ashburner, M. et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. 2000;25: 25–29 .
8. Bader, G. D. et al. BIND—The Biomolecular Interaction Network Database. Nucleic Acids Res.2001; 29, 242–245.
9. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28:27-30.
10. Afendi FM, Okada T,Yamazaki M, et al. KNApSAcK Family database: integrated metabolite- plant species data-bases for multifaceted plant research. Plant Cell Physiol 2012;53:e1.
11. Loub WD, Farnsworth NR, soejarto DD, et al. NAPRALERT: computer handing of natural product research data. J Chem Inform Comput Sci 1985;25:99-103.
12. Wang JF, Zhou H, Han LY, et al. Traditional Chinese medicine information database. Clin Pharmacol Therap 2005; 78:92-93.
13. Ji ZL, Zhou H, Wang JF, et al. Traditional Chinese medicine information database. J Ethnopharmacol 2006;103:501.
14. Chen X, Zhou H, Liu YB, et al. Database of Traditional Chinese medicine and its application to studies of mechanism and to prescription validation. Br J Pharmacol 2006;149:1092-03.
15. Ye H, Ye L, Kang H, et al. HIT: linking herbal active ingredients to targets. Nucleic Acids Res 2011;39:D1055-1059.
16. Liu X, Zhu F, Ma X, et al. The Therapeutic Target Database: an internet resource for the primary targets of approved, clinical trial and experimental drugs. Exp Opin Therapeutic Targets 2011;15:903-12.
17. Bernstein FC, Koetzle TF, Williams GJ, et al. The Protein Data Bank a computer- based archival file for macromolecular structures. Arch Biochem Biophys 1978;185:584-91.
18. Magrane M, Consortium U. UniProt Knowledgebase: a hub of integrated protein data. Database J Biol Databases Curation 2011;2011:bar009.
19. Punta M, Coggill PC, Eberhardt RY, et al. The Pfam protein families database. Nucleic Acids Res 2012;40:D290-301.
20. Joshi RK, Kar B , Nayak S. Exploiting EST database for the mining and characterization of short sequences repeat (SSR) markers in Catharanthus roseus L. Bioinformation 2011;5:378-81.
21. Wang CM, Liu P, Yi C, et al. A first generation microsatellite and SNP- based linkage map of Jatrpha. Plos One 2011;6:e236-32.
22. Venuprasad R, Bool ME, Quiatchon L, et al. A QTL for rice grain yield in aerobic environments with large effects in three genetic backgrounds, TAG. Theoretical and applied genetics. Theoretische Und Angewandte Genetic 2011;124(2):323-332.
23. Varshey RK, Graner A, Scrrells ME. Genic microsatellite markers in plants: features and applications. Trends Biotechnol 2005;23:48-55.
24. Prinzing AJ, Durka W, Klotz S, et al. The niche of higher plants: evidence for phylogenetic conversation. Proc Biol Sci Roy Soc 2001;268:2383-9.
25. Ronsted N, Savolainen V, Molgaard P, Jager AK. Phylogenetic selection of Narcissus species for drug discovery. Biochem Sys Ecol 2008;36:417-22.
26. Petersen RK, Christensen KB, Assimopoulou AN, et al. Parmacophore-driven identification of PPAR gamma agonists from natural sources. J Comp Aided Mol Design 2011;25:107-16.
27. Shoichet BK. Virtual screening of chemical libraries. Nature 2004;432;862-5.
28. Rondas D, Bugliani M, D’Hertog W, Lage K, Masini M, et al. Glucagon-like peptide-1 protects human islets against cytokine- mediated βcell dysfunction and death. A proteomic study of the pathway involved. J Proteome Res 2013;12(9):4193-4206.
29. Newsholme P, de Bittencourt PI. The fat cell senescence hypothesis: a mechanism responsible for abrogating the resolution of inflammation in chronic disease. Curr Opin Clin Nutr Metab Care 2014;17(4): 295-305.
30. Kiess W, Petzol S, Topfer M, Garten A, Bluher S, et al. Adipocytes and adipose tissue. Best Pract Res Clin Endocrinol Metab 2008;22(1):135-153.
31. Lour, D, Venkov P, Zlatanova J. Faseb J 1995;9:777-787.
32. Peng G, Hopper J. E Mol. Cell. Biol 2000;20:5140-5148.
33. Peng G, Hopper J. E. Proc.Natl.Acad.Sci USA 2002;99:8548-8553.
34. Aebersold R. Nature 2003;422:115-116.
Published
2017-09-14
Section
Review Article