Bioinformatics Lab and Diabetes Mellitus: A Review

Authors

  • Talib Yusuf  Department of Biotechnology, MGM Institute of Health Sciences,Aurangbad, Maharshtra, India
  • S. H. Talib  Department of Medicine, MGM Medical College and hospital, Aurangbad, Maharshtra, India

Keywords:

sequence similarity, Molecular Docking, Diabetes Mellitus, optimization technique, Biological databases.

Abstract

This Diabetes is a metabolic disorder that occurs when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces. It is classified into two basic forms Type I and Type II diabetes. Bioinformatics Laboratory comprises of various techniques like sequence similarity, structural analysis, molecular Docking, optimization technique certain advanced algorithms. Computer-assisted drug design approach has contributed to the successful discovery of several novel antidiabetic agents. Molecular Docking continues to be a great promise in the field of computer based drug design. Several simulation models have been proposed to study the physiology and pathophysiology of diabetes. Biological databases and Atlas plays an important role in getting up-to-date global report on diabetes. Like so many other areas of medicine, Bioinformatics has had a profound impact on diabetes research.

References

  1. Agrawal, S., Dimitrova, N., Nathan, P., Udayakumar, K., Lakshmi, S.S., Sriram, S., Manjusha, N., and Sengupta, U. (2008), T2D-Db: an integrated platform to study the molecular basis of Type 2 diabetes. BMC Genomics, 9: 320.
  2. Babad, J., Geliebter, A. and DiLorenzo, (2010), T.P. T-cell autoantigens in the non-obese diabetic mouse model of autoimmune diabete. Immunology, 131(4): 459-465.
  3. Balamurugan, A.N., Miyamoto, M., Wang, W., Inoue K. and Tabata, Y. (2003), Streptozotocin (STZ) used to induce diabetes in animal models. J. Ethnopharm., 26: 102-103.
  4. Bayraktar, C., Karan, O., Gümü?kaya, H., Karlik, B. (2010), Electrical, Electronics and Computer Engineering (ELECO), National Conference, Bursa, 603-607.
  5. Bellazzi, R., Larizza, C., Montani, S., Riva, A., Stefanelli, M., et al (2002),. A telemedicine support for diabetes management: the T-IDDM project. Comput. Methods Programs Biomed., 69: 147-161.
  6. Bergman, R.N., Ider, Y.Z., Bowden, C.R., and Cobelli, C. (1979), Quantitative estimation of insulin sensitivity. Am. J. Physiol., 236: E667-E677.
  7. Boykoe, E.J., and Lipsky, B.A. (1995), Infection and diabetes mellitus. In Diabetes in America. Edited by Harris MI. Washington DC: National Institutes of Health., 485-496.
  8. Cavasotto, C.N., and Orry, A.J. (2007), Ligand docking and structure-based virtual screening in drug discovery. Curr. Top. Med. Chem., 7: 1006-1014.
  9. Chatenoud, L., Thervet, E., Primo, J., Bach, J.F. (1994), Anti-CD3 antibody induces long-term remission of overt autoimmunity in nonobese diabetic mice. Proc Natl Acad Sci U S A., 91: 123-127.
  10. Dalla, M.C., Rizza, R.A., and Cobelli C. (2007), Meal simulation model of the glucose-insulin system. IEEE Trans Biomed Eng., 54: 1740-1749.
  11. Federiuk, I.F., Casey, H.M., Quinn, M.J., Wood, M.D., Ward, W.K. (2004), Induction of type 1 diabetes mellitus in laboratory rats by use of alloxan; route of administration, pitfalls, and insulin treatment. Comprehensive Medicine, 54: 252-257.
  12. Feig, M., Onufriev, A., Lee, M.S., Im, W., Case, D.A., and Brooks, C.L. (2004), Performance comparison of generalized born and Poisson methods in the calculation of electrostatic solvation energies for protein structures. J. Comp. Chem., 25: 265-284.
  13. Ganugapati, J., Mukkavalli, S., and Sahithi, A., (2011), Docking Studies of Green Tea Flavonoids as Insulin Mimetics. IJCA, 30: 48-51.
  14. Guttula, S.V., Appa Rao, A., Sridhar G.R., and Chakravarthy, M.S. (2011), Protein ligand interaction analysis an insilico potential drug target identification in diabetes mellitus and nephropathy. JBSA, 2: 95-99.
  15. Hostetler, H.A., Syler, L.R., Hall, L.N., Zhu, G., Schroeder, F., and Kier, A.B., (2008), A Novel High-Throughput Screening Assay for Putative Antidiabetic Agents through PPARα Interactions. J. Biomol. Screen, 13: 855-861.
  16. Kitchen, D.B., Decornez, H., Furr, J.R., and Bajorath, J. (2004), Docking and scoring in virtual screening for drug discovery: methods and applications. Nature reviews Drug discovery, 3: 935-949.
  17. Lee, H.J., Lee, S.H., Ha, K.S. et al., (2009), Ubiquitous healthcare service using Zigbee and mobile phone for elderly patients. Int J Med Inform, 78: 193-198.
  18. Lehmann, E.D. (1999), Experience with the Internet release of AIDA v4 – an interactive educational diabetes simulator. Diabetes Technol Ther., 1: 41-54.
  19. Lengauer, T., and Rarey, M. (1996), Computational methods for biomolecular docking. Curr. Opin. Struct. Biol., 6: 402-406.
  20. Lim, J.E., Hong, K., Jin, H., Kim, Y.S., Park, H.K., and Oh, B., (2010), Type 2 diabetes genetic association database manually curated for the study design and odds ratio. BMC Med. Inform. Decis. Mak., 10: 76.
  21. Malaisse, W.J. (2003), Pharmacology of the meglitinide analogs: new treatment option for type 2 diabetes mellitus. Treat. Endocrinol., 2: 401-414.
  22. McInnes, C. (2007), Virtual screening strategies in drug discovery. Curr. Opin. Chem. Biol., 11: 494-502.
  23. Middha, S.K., Mittal, Y., Usha, T., Kumar, D., Srinivasan, R., Vashisth, L., Bhattacahrgae, B., and Nagaveni, M. B., (2009), Phyto-mellitus:, A phyto-chemical database for diabetes. Bioinformation, 4: 78-79.
  24. Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., and Olson, A.J. (1998), Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comp. Chem., 19: 1639-1662.
  25. Notkins, A.L. (2002), Immunologic and genetic factors in type 1 diabetes. J. Biol. Chem.,277: 43545-43548.
  26. Perfetti, R., Barnett, P.S., Mathur, R., and Egan, J. (1998), M. Novel therapeutic strategies for the treatment of type 2 diabetes. Diabetes Metab. Rev., 14: 207-225.
  27. Rao, A.A., Hanuman, M.T., Changalasetty, S.B., Thota, L.S., and Gedela, S. (2008), Bioinformatic Analysis of Functional Proteins Involved in Obesity Associated with Diabetes. Int. J. Biomed. Sci., 4: 70-73.
  28. Rao, A.A., Sridhar, G.R., Srinivas, B., and Das, U.N. (2008), Bioinformatics analysis of functional protein sequences reveals a role for brain-derived neurotrophic factor in obesity and type 2 diabetes mellitus. Med Hypotheses, 70: 424-429.
  29. Rao, A.A., Thota, H., Adapala, R., Changalasetty, S.B., Gumpeny R.S., Akula, A.,Thota, L.S., Challa, S.R., Rao, N., and Das, U.N. (2008), Proteomic Analysis in Diabetic Cardiomyopathy using Bioinformatics Approach. Bioinf. Biol. Insights., 2: 1-4.
  30. Ritchie, D.W. (2003), Evaluation of Protein Docking Predictions Using Hex 3.1 in CAPRI Rounds 1 and 2. PROTEINS: Struct. Funct. Genet., 52: 98-106.
  31. Semighini, E.P., Resende, J.A., de Andrade, P., Morais, P.A., Carvalho, I., Taft, C.A., and Silva, C.H. (2011), Using computer-aided drug design and medicinal chemistry strategies in the fight against diabetes. J Biomol.Struct. Dyn., 28: 787-796.
  32. Shakil, S., and Khan, A.U (2010),. Infected foot ulcers in male and female diabetic patients: a clinic-bioinformative study. Ann. Clin. Microbiol. Antimicrob., 9: 2.
  33. Shea, S. (2007), The Informatics for Diabetes and Education Telemedicine (IDEATel) Project. Trans Am Clin Climatol Assoc., 118: 289-304.
  34. Sivaprasad, S., and Jackson, H. (2007), Blood pressure control in type II diabetics with diabetic retinopathy. Eye, 21: 708-711.
  35. Smith, G.R., and Sternberg, M.J (2002), Prediction of protein–protein interactions by docking methods. Curr. Opinion Struct. Biol., 12: 28-35.
  36. Spero, M., Kenet, A., Porter (1998), B. Effective Clinical Practice., 1: 90-92.
  37. Sun, H (2008), Pharmacophore-based virtual screening. Curr. Med. Chem.,15: 1018-1024.
  38. Wild, S., Roglic, G., Green, A., Sicree, R., King, H (2004), Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care, 27: 1047-1053.
  39. Yanarday, R., Colac, H. (1998), Effect chard (Beta vulgaris L.varcicla ) on blood glucose level in normal and alloxan induced diabetic rabbit. J. Ethnopham., 4: 309-311.

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Published

2018-04-30

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Research Articles

How to Cite

[1]
Talib Yusuf, S. H. Talib, " Bioinformatics Lab and Diabetes Mellitus: A Review, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 5, pp.1192-1200, March-April-2018.