Abstract

Research Article

In silico analysis and characterization of fresh water fish ATPases and homology modelling

Rumpi Ghosh, AD Upadhayay and AK Roy*

Published: 11 October, 2017 | Volume 1 - Issue 1 | Pages: 018-024

ATPases is known to be a crucial in many biological activities of organisms. In this study, physicochemical properties and modeling of ATPases protein of fish was analysed using In silico approach. ATPases a protein selected from fish species, including Gold fish (Carassius auratus auratus), Zebra fish (Hypancistrus zebra), White fishes (Coregonus autumnalis), Grass carp (Ctenopharyngodon idella) and Anabas testudineus (Koi) were used in this study. Physicochemical characteristics showed with molecular weight (25045.58-25148.57Da), theoretical isoelectric point (9.30-9.97), extinction coefficient(26470-34950), aliphatic index(147.31-150.35), instability index(32.84-42.67), total number of negatively charged residues and positively charged residues (5/7-6/8), and grand average of hydropathicity (1.014-1.151) were computed. All proteins were classified as transmembrane proteins. In secondary structure prediction, all proteins were composed of random coils as predominant, followed by extended strands, alpha helix and beta turn. Three dimensional structure of protein were predicted and verified as good structures. All model structures were evaluated being accepted and reliable based on structural evaluation and stereo chemical analysis.

Read Full Article HTML DOI: 10.29328/journal.hpbr.1001003 Cite this Article Read Full Article PDF

Keywords:

ATPase; Expasy’s prot; Physicochemical characterisation; Clustal W; Modelling etc

References

  1. Tran Ngoc Tuan, Pham Minh Duc. In silico analysis of freshwater fish major histocompatibility complex Class II Alpha. Asia pacific journal of science and technology. 2016; 21: 1-8. Ref.: https://goo.gl/MtFwo3
  2. Tran Ngoc Tuan, Wang Gui-Tang, Pham Minh duc. Tumor necrosis factor alpha of teleosts: In silico characterization and homology modeling. Songklanakarin Journal of Science & Technology. 2016; 38: 549-557. Ref.: https://goo.gl/YB3dgD
  3. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Molecular biology and Evolution. 2013; 30: 2725-2729. Ref.: https://goo.gl/PYMja7
  4. Hogg PJ. Disulfide bonds as switches for protein function. Trends Biochem Sci. 2003; 28: 210-214. Ref.: https://goo.gl/5EevyN
  5. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Computer application in bioscience. 1995; 11: 681-684. Ref.: https://goo.gl/wUsYbn
  6. Schwede T, Kopp J, Guex N, Peitsch MC. Swiss-MODEL:An automated protein homology modeling server. Nucleic acids research. 2003; 31: 3381-3385. Ref.: https://goo.gl/MjY825
  7. Arnold K, Bordoli L, Kopp J, Schwede T. The Swiss-Model workspace: A web-based environment for protein structure homology modelling. Bioinformatics. 2006; 22: 195-201. Ref.: https://goo.gl/tTxBtk
  8. Fiser A. Template-based Protein structure modeling. In computational Biology. 2010; 73-94.
  9. Template-based protein structure modeling. D Fenyo. Computational Biology: Humana press; 2004.
  10. Ramachandran GN, Ramakrisnan C, Sasisekhran V. Stereochemistry of polypeptide chain configurations. J Mol Biol. 1963; 7: 95-99. Ref.: https://goo.gl/BaHh8q
  11. Laskowski RA, Rullman JAC, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK_NMR: Programs for checking the quality of protein structures solved by NMR. Journal of bimolecular NMR. 1996; 8: 477-486. Ref.: https://goo.gl/7Emfju
  12. Ramachandran G, Ramakrishnan c, Sasisekharan V. Stereochemistry of polypeptide chain configurations. J Mol Biol. 1963; 7: 95-99. Ref.: https://goo.gl/W7vzZ9
  13. Wallner B, Elofsson A. Can correct protein models be identified? Protein science. 2003; 12: 1073-1086. Ref.: https://goo.gl/VrWtw2
  14. Wiederstein M, Sippl MJ. ProSA-web: Interactive web service for the recognition of errors in three dimensional structures of proteins. Nucleic Acids Res. 2007; 35: W407-W410. Ref.: https://goo.gl/QFtmm8
  15. Sippl MJ. Recognition of errors in three dimensional structures of proteins. Proteins: structure, function and genetics. 1993; 17: 355-362. Ref.: https://goo.gl/cqfoDW
  16. Yadav NK, Shukla P, Parrek S, Singh R. Structure prediction and functional characterization of matrix protein of human Metapneumovirus (Strain can97-83)(hMPV). International Journal of advanced Life sciences. 2013; 6: 538-548. https://goo.gl/GDGdsz
  17. Ghosh R, Upadhayay AD, Roy AK. In silico Analysis, structure modelling and Phosphorylation site prediction of Vitellogenin Protein from Gibelion catla. Journal of applied Biotechnology and bioengineering. 2017; 3: 55.
  18. Pradeep N, Anupama A,Vidyashree K, Lakshmi P. In silico characterization of industrial important cellulases using computational tools. Advances science and Tecnology. 2012; 48: 14.
  19. Rumpi G, Upadhyay AD, Roy AK, Samik A. Structural analysis of cytochrome C Genes of Major Carp and utility of Forensic Investigation. Journal of Forensic and crime investigation. 2017; 1: 103. Ref.: https://goo.gl/ALvudA
  20. Tuan TN, WeiMin W, Duc PM. Characterization and homology modeling of finfish NK-kappa B inhibitor alpha using In silico analysis. J Sci Develop. 2015; 13: 216-225. Ref.: https://goo.gl/gqRHkP
  21. Ghosh R, Upadhayay AD, Roy AK, Mamta singh. In silico Analysis and 3d Structure Prediction of mitochondrial RHO GTPase 2 Protein of Danio rerio (zebra fish) by Homology Modeling. Jacobs J Bioinform Proteom. 2016; 1: 006. Ref.: https://goo.gl/VPmbwg
  22. Ghosh R, Upadhayay AD, Roy AK.  In silico analysis of Rag 1 Protein of Labeo calbasu. International Journal of Innovative Research in Science & Engineering. 4: 136. Ref.: https://goo.gl/Wmo4w8

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