Asian Journal of Computer Science and Technology (AJCST)
Prediction of Secondary Structures of Human Prion Proteins using Miscellaneous MethodsAuthor : Ahmad Aftab Khan and Kalpana Sharma
Volume 8 No.1 January-March 2019 pp 36-41
The contemporary study is an attempt to predict the secondary structures of proteins, from the dataset of human prion proteins which has been acquired from NCBI repository. In this context, we have exploited PSIPRED server which is considered to be proficient and impulsive method to protein structure prediction where users can submit the query sequence of their desire and receive the results of prediction both textually and eloquently. Furthermore, Phyre2 was applied across the amino acid sequence which is among the most widely server deployed for generating consistent protein models characterizing the prediction of protein structures. Moreover, two feed forward neural networks with a sole hidden layer which was tested and trained on a 10 fold cross validation mechanism in MATLAB, and subsequently significant prediction accuracy of 71.73% and minimum mean absolute error of 12.3% was achieved.
Protein Structure Prediction, PSIPRED, Phyre2, BLAST, PSIBLAST
 E. Buxbaum, “Fundamentals of Protein Structure and Function”, Springer, ISBN: 978-0-387-26352-6, 2007.
 D. Ofer, and Y. Zhou, “Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large‐scale training”, Proteins: Structure, Function, and Bioinformatics, Vol. 66, No. 4, pp. 838-845, 2007.
 A. Rafał. “Dimensionality reduction of pssm matrix and its influence on secondary structure and relative solvent accessibility predictions”, World Academy of Science, Engineering And Technology, Vol. 58, pp. 657-664, 2009.
 B. Rost, “Review: protein secondary structure prediction continues to rise”, J StructBiol, Vol. 134, No. 2-3, pp. 204-18, 2001.
 W. Kabsh and C. Sander, “How good are predictions of protein secondary structure”, FEBS Letters, Vol. 155, pp. 179-182, 1983.
 U. Y. Fadime, Y. O¨zlem, and T. Metin, “Prediction of secondary structures of proteinsnext term using a two-stage method”, Computers & Chemical Engineering, Vol. 32, No. 1-2, pp. 78-88, 2008.
 J. A. Cuff, and G. J. Barton, “Application of multiple sequence alignment profiles to improve protein secondary structure prediction”, Proteins, Vol. 40, No. 3, pp. 502-11, 2000.
 B. Rost and C. Sander, “Prediction of protein secondary structure at better than70% accuracy”, J MolBiol, Vol. 232, pp. 584-599, 1993.
 R. D. King and M. J. E. Sternberg, “Identification and application of the concepts important for accurate and reliable protein secondary structure prediction”, Protein Sci, Vol. 5, pp. 2298–2310, 1996.
 D. Frishman and P. Argos, “Seventy-five percent accuracy in protein secondary structure prediction”, Proteins, Vol. 27, pp. 329–335, 1997.
 A. A. Salamov and V. V. Solovyev, “Prediction of protein secondary structure bycombining nearest-neighbor algorithms and multiple sequence alignments”, J MolBiol, Vol. 247, pp. 11–15, 1995.
 H. Hu, Y. Pan, R. Harrison, and P. Tai, “Improved protein secondary structure prediction using support vector machine and a new encoding scheme and an advanced tertiary classifier”, IEEE Trans. NanoBiosci. Vol. 3, pp. 265–271, 2004.
 H. Kim, and H. Park, “Protein secondary structure prediction based on an improved support vector machines approach”, Protein Eng. Vol. 16, pp. 553-560, 2003.
 N. Nguyen, and J. C. Rajapakse, “Two stage support vector machines for proteinsecondary structure prediction”, Intl J Data Mining & Bioinformatics, Vol. 1, pp. 248-269, 2007.
 S.F. Altschul, T.L. Madden, A.A. Schaffer, J. Zhang, Z. Zhang, W. Miller, and D.J. Lipman, (1997) , “Gapped BLAST and PSIBLAST: a new generation of Protein database search programs”, Nucl. Acids Res. Vol. 25, pp. 3389-3402, 1997.
 C. A. Orengo, J.E, Bray, T. Hubbard, L. LoConte, and I. Sillitoe, “Analysis and assessment of ab initio three dimensional prediction, secondary structure, and contacts prediction”, PROTEINS Suppl. Vol. 3, pp. 149-170, 1999.