Author(s): Roma Chandra


DOI: 10.52711/0974-360X.2021.01080   

Address: Roma Chandra
Assistant Professor, Department of Biotechnology, IILM College of Engineering & Technology, Greater Noida, Uttar Pradesh, India.
*Corresponding Author

Published In:   Volume - 14,      Issue - 12,     Year - 2021

Protein structure prediction is one of the important goals in the area of bioinformatics and biotechnology. Prediction methods include structure prediction of both secondary and tertiary structures of protein. Protein secondary structure prediction infers knowledge related to presence of helixes, sheets and coils in a polypeptide chain whereas protein tertiary structure prediction infers knowledge related to three dimensional structures of proteins. Protein secondary structures represent the possible motifs or regular expressions represented as patterns that are predicted from primary protein sequence in the form of alpha helix, betastr and and coils. The secondary structure prediction is useful as it infers information related to the structure and function of unknown protein sequence. There are various secondary structure prediction methods used to predict about helixes, sheets and coils. Based on these methods there are various prediction tools under study. This study includes prediction of hemoglobin using various tools. The results produced inferred knowledge with reference to percentage of amino acids participating to produce helices, sheets and coils. PHD and DSC produced the best of the results out of all the tools used.

Cite this article:
Roma Chandra. In Silico Study of Secondary Structure of Hemoglobin Protein. Research Journal of Pharmacy and Technology. 2021; 14(12):6245-9. doi: 10.52711/0974-360X.2021.01080

Roma Chandra. In Silico Study of Secondary Structure of Hemoglobin Protein. Research Journal of Pharmacy and Technology. 2021; 14(12):6245-9. doi: 10.52711/0974-360X.2021.01080   Available on:

1.    Rost B, Sander C, Schneider R. Redefining the goals of protein secondary structure prediction. J Mol Biol. 1994; 235:13–26.
2.    Hang Chen, Fei Gu and Zhengge Huang Improved Chou-Fasman method for protein secondary structure prediction.BMC Bioinformatics.2006, 7(Suppl 4):S14.
3.    Salzberg, S. and Cost, S. Predicting Protein Secondary Structure with a Nearest-neighbor Algorithm. Journal of Molecular Biology.1992; 227:371–374.
4.    Martin J, Gibrat JF, Rodolphe F. Analysis of an optimal hidden markov model for secondary structure prediction. BMC Struct Biol. 2006; 6:25. 30.
5.    Won KJ, Hamelryck T, Prügel-Bennett A, Krogh A. An evolutionary method for learning HMM structure: prediction of protein secondary structure. BMC Bioinformatics. 2007; 8:357.
6.    Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol. 1996; 266:540–53.
7.    Sen TZ, Jernigan RL, Garnier J, Kloczkowski A, GOR V. server for protein secondary structure prediction. Bioinformatics. 2005; 21:2787–8.
8.    Lin K, Simossis VA, Taylor WR, Heringa J. A simple and fast secondary structure prediction method using hidden neural networks.Bioinformatics. 2005; 21:152–9. 29.
9.    Cuff, J. A., Clamp, M. E., and Barton, G. J.JPred: A consensus secondary structure prediction server. Bioinformatics .1998; 14:892–893.
10.    Geourjon C., Deléage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple sequences Comput. Appl. Biosci.1995; 11: 681-684.

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RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

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