Author(s): Akshay R. Yadav, Shrinivas K. Mohite

Email(s): akshayyadav24197@gmail.com

DOI: 10.5958/0975-4377.2020.00052.X   

Address: Akshay R. Yadav*, Dr. Shrinivas K. Mohite
Department of Pharmaceutical Chemistry, Rajarambapu College of Pharmacy, Kasegaon, Sangli, Maharashtra, India- 415404.
*Corresponding Author

Published In:   Volume - 12,      Issue - 4,     Year - 2020


ABSTRACT:
The ultimate goal of protein modeling is to predict a structure from its sequence with accuracy equivalent to the best experimentally obtained results. In all contexts where today only experimental structures provide solid foundations, it would allow users to use quickly in-silico protein models safely: structural drug design, protein function analysis, interacting, antigenic behaviors and the rational conception of proteins with increased stability or novel functions. Moreover, protein models can only be obtained if experimental techniques fail. Many proteins are just too large for an NMR test, and for X-ray diffraction they can't crystallize. In cases where there are difficulty in obtaining experimental structures for a given protein, the comparative modeling of protein structures offers an efficient alternative to determining experimental structure. Normally a model with an estimated RMSD of 1 to 4 to the experimental structure may be obtained if you find a structural template that is more than 50% identical to the query sequence.


Cite this article:
Akshay R. Yadav, Shrinivas K. Mohite. Homology Modeling and Generation of 3D-structure of Protein. Res. J. Pharma. Dosage Forms and Tech.2020; 12(4):313-320. doi: 10.5958/0975-4377.2020.00052.X


REFERENCES:
1.    Hubner Z, Arakaki A, Skolnick J. Ontheoriginandhighly likely completeness of single-domain protein structures. Proc. Natl. Acad. Sci. 2006; 103: 2605–2610.
2.    Todd, A.E., Orengo, C.A., Thornton, J. M. Evolution off unction in protein super families, from a structural perspective. J. Mol. Biol. 2001; 307: 1113–1143.
3.    Pieper, U., Webb, B.M., Barkan, D.T., Schneidman-Duhovny, D., Schlessinger, A., Braberg, H., Yang, Z., Meng, E.C., Pettersen, E.F., Huang, C.C. ModBase, adatabase of annotated comparative protein structure models, and associated resources. Nucleic Acids Res. 2011; 39: 465–474.
4.    Kiefer, F., Arnold, K., Kunzli, M., Bordoli, L., Schwede, T. The SWISS-MODEL Repository and associated resources. Nucleic Acids Res. 2009; 37: 387–392.
5.    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 2000; 28: 235–24.
6.    Chandonia, J.M., Brenner, S.E. The impact of structural genomics: expectations and outcomes. Sci. 2006; 311: 347–351.
7.    Becker, O.M., Dhanoa, D.S., Marantz, Y., Chen, D., Shacham, S., Cheruku, S., Heifetz, A., Mohanty, P., Fichman, M., Sharadendu, A. An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression. J. Med. Chem. 2006; 49: 3116–3135.
8.    Brylinski, M., Skolnick, J.: Q-Dock: low-resolution flexible ligand docking with pocketspecific threading restraints. J. Comput. Chem. 2008; 29: 1574–1588.
9.    Ekins, S., Mestres, J., Testa, B. In silico pharmacology for drug discovery: applications to targets and beyond. Br. J. Pharmacol. 2007; 152: 21–37.
10.    Labro, A.J., Boulet, I.R., Choveau, F.S., Mayeur, E., Bruyns, T., Loussouarn, G., Raes, A.L., Snyders, D.J.: The S4-S5 linker of KCNQ1 channels forms a structural scaffold with the S6 segment controlling gate closure. J. Biol. Chem. 2011; 286: 717–725.
11.    Szklarz, G.D., Halpert, J.R. Use of homology modeling in conjunction with site-directed mutagenesis for analysis of structure-function relationships of mammalian cytochromes P450. Life Sci. 1997; 61: 2507–2520.
12.    Claude, J.B., Suhre, K., Notredame, C., Claverie, J.M., Abergel, C.: CaspR: a web server for automated molecular replacement using homology modelling. Nucleic Acids Res. 2004; 32: 606–609.
13.    Dong, J., Yang, G., McHaourab, H. Structural basis of energy transduction in the transport cycle of MsbA. Sci. 2005; 308: 1023–1028.
14.    Sander C, Schneider R. Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins. 1998; 9: 56–68.
15.    Simons KT, Bonneau R, Ruczinski I, Baker D. Ab initio structure prediction of CASP III targets using ROSETTA. Proteins. 1999; 3 :171–176.
16.    Sippl MJ. Calculation of conformational ensembles from potentials of mean force. J Mol Biol. 1990; 213: 859–862.
17.    Stites WE, Meeker AK, Shortle D. Evidence for strained interactions between side-chains and the polypeptide backbone. J Mol Biol. 1994; 235: 27–32.
18.    Tappura K. Influence of rotational energy barriers to the conformational search of protein loops in molecular dynamics and ranking the conformations. Proteins. 2001; 44: 167–79.
19.    Taylor WR. Identification of protein sequence homology by consensus template alignment. J Mol Biol. 1986; 188:233–258.
20.    Thompson JD, Higgins DG, Gibson TJ. ClustalW: improving the sensitivity of progressive multiple sequence alignments through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994; 22:4673–4680.
21.    Vriend G. WHAT IF-A molecular modeling and drug design program. J Molec Graphics. 1994; 8:52–56.
22.    Wang J, Cieplak P, Kollman PA. How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem. 2000; 21:1049–1074.

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