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A Comparative Study of Homology Modeling Algorithms for NPTX2 Structure Prediction


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1 Department of Biotechnology, School of Life Sciences, Vels University, Pallavaram, Chennai, Tamil Nadu, India
     

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Alzheimer’s Disease (AD) is a very prevalent neurological disorder that results in loss of memory due to the weakening of synapses. Down-regulation of neuronal Pentraxin (NPTX2), a secretory protein is one of the causes for AD. The structure of a protein is very important to predict the protein’s function. The experimental structure of NTPX2 is not available yet. Hence in this study, three structures for NPTX2 were generated using Geno3D, Modeller9.20 and Swiss Model. The quality of the protein was validated using PROCHECK and ERRAT. The PROCHECK results for the structures modeled using Geno3D, Modeller, and Swiss Model showed 63%, 87.3% and 88.2% residues in the most favoured regions and 2.5%, 0.0%, 0.00% residues in disallowed regions respectively. The ERRAT results showed an overall quality factor of 90.27, 60.476, 77.54 respectively. The model generated from Swiss Model can be considered as best model because in PROCHECK the model showed high number of residues in the most favoured region and no residues were in the disallowed region. The ERRAT result also showed an overall quality factor greater than 75. Though Geno3D showed a good overall quality factor, it showed about 2.5% residues in the disallowed region. Structure Modeled using Modeller showed good results for Ramachandran plot with no residues in the disallowed region. However, its overall quality factor was only 60.476. These structures predicted can lay a foundation for discovering new drugs for the treatment of AD.

Keywords

Alzheimer’s Disease, Neuronal Pentraxin, Geno3D, Modeller, Swiss Model.
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  • Kelley BJ, Petersen RC. Alzheimer's disease and mild cognitive impairment. Neurol Clin. 2007;25(3):577-609.

  • Xiao MF, Xu D, Craig MT, et al. NPTX2 and cognitive dysfunction in Alzheimer's Disease. Elife. 2017;6:1-27.

  • Vyas VK, Ukawala RD, Ghate M, Chintha C. Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci. 2012;74(1):1-17.

  • Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2015;44(D1):D7-19.

  • Gasteiger E. Protein Identification and Analysis Tools on the ExPASy Server. In: John M. Walker ed, The Proteomics Protocols Handbook, Humana Pres. 2005: 571-607.

  • Gill SC, Von Hippel PH. Calculation of protein extinction coefficients from amino acid sequence data. Anal Biochem. 1989;182: 319- 326.

  • Ikai AJ. Thermo stability and aliphatic index of globular proteins. J Biochem. 1980; 88: 1895-1898

  • Kyte J, Doolottle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982; 157: 105- 132.

  • Hirokawa T, Boon-Chieng S, Mitaku S. SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics. 1998;14(4): 378–379.

  • CYS_REC. http://sun1.softberry.com/berry.phtml?topic= cys_rec&group=help &subgroup=propt. (27/10/2006)

  • Geourjon C, Deléage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics. 1995; 11(6): 681–684.

  • Sali A, Blundelll TL. Comparative protein modeling by satisfaction of spatial restraints. J Mol Biol. 1993; 234: 779-815

  • Combet C, Jambon M, Deleage G, Geourjon C. Geno3D: Automatic comparative molecular modelling of protein. Bioinformatics. 2002; 18: 213-214.

  • 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.

  • Ramachandran GN, Ramakrishnan C, Sasisekhran V. Stereochemistry of polypeptide chain confi guarations. J Mol Biol. 1963; 7: 95-99.

  • Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993; 9 :1511-1519.

  • Yang YH, Dai L, Xia HC, Zhu KM, Liu HJ, Chen KP: Protein profile of rice (Oryza sativa) seeds. Genet Mol Biol. 2013, 36 (1): 87-92.

  • Geourjon C, Deléage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci. 1995:11; 681-684.

  • Eswar N, Webb B, Marti-Renom MA, et al. Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics. 2006; Chapter 5:Unit-5.6.

  • Essoussi N, Boujenfa K, Limam M. A comparison of MSA tools. Bioinformation. 2008;2(10):452-5.

  • Nicolas G, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 1997; 18: 2714-2723.


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  • A Comparative Study of Homology Modeling Algorithms for NPTX2 Structure Prediction

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Authors

H. Sowmya
Department of Biotechnology, School of Life Sciences, Vels University, Pallavaram, Chennai, Tamil Nadu, India

Abstract


Alzheimer’s Disease (AD) is a very prevalent neurological disorder that results in loss of memory due to the weakening of synapses. Down-regulation of neuronal Pentraxin (NPTX2), a secretory protein is one of the causes for AD. The structure of a protein is very important to predict the protein’s function. The experimental structure of NTPX2 is not available yet. Hence in this study, three structures for NPTX2 were generated using Geno3D, Modeller9.20 and Swiss Model. The quality of the protein was validated using PROCHECK and ERRAT. The PROCHECK results for the structures modeled using Geno3D, Modeller, and Swiss Model showed 63%, 87.3% and 88.2% residues in the most favoured regions and 2.5%, 0.0%, 0.00% residues in disallowed regions respectively. The ERRAT results showed an overall quality factor of 90.27, 60.476, 77.54 respectively. The model generated from Swiss Model can be considered as best model because in PROCHECK the model showed high number of residues in the most favoured region and no residues were in the disallowed region. The ERRAT result also showed an overall quality factor greater than 75. Though Geno3D showed a good overall quality factor, it showed about 2.5% residues in the disallowed region. Structure Modeled using Modeller showed good results for Ramachandran plot with no residues in the disallowed region. However, its overall quality factor was only 60.476. These structures predicted can lay a foundation for discovering new drugs for the treatment of AD.

Keywords


Alzheimer’s Disease, Neuronal Pentraxin, Geno3D, Modeller, Swiss Model.

References