IJCRR - 5(12), June, 2013
FUNCTION PREDICTION USING CLUSTER ANALYSIS OF UNANNOTATED ALIGN SEQUENCES
Author: Anjan Kumar Payra
Proteins are responsible for nearly every task of cellular life, including cell shape and inner organization, product manufacture and waste cleanup, and routine maintenance. Proteins also receive signals from outside the cell and mobilize intracellular response. Experimental procedures for protein function prediction are inherently low throughput and are thus unable to annotate a non-trivial fraction of proteins that are becoming available due to rapid advances in genome sequencing technology . This has motivated the development of computational techniques that utilize a variety of high-throughput experimental data for protein function prediction. So, there is need to design algorithm to find similar functional proteomic sequence from large set of sequence database. Here we present a novel unsupervised method, called Function Finder (in short F-Func) for identification function of unannotated proteomic sequence. F-Func uses clustering of sequence information represented by numerical features, performing filtering, assigned score and meet with the criterion produces decision. Using help of producing result estimate success rate of F-Func method. Estimated success rate of F-Func methods, which is almost 70%.
Keywords: Sequence, Homology, motif, F-Func, Prediction, Cluster.
Anjan Kumar Payra. FUNCTION PREDICTION USING CLUSTER ANALYSIS OF UNANNOTATED ALIGN SEQUENCES International Journal of Current Research and Review. 5(12), June, 134-145
1. Bork, P. and Koonin, E.V. 1998. Predicting functions from protein sequences—Where are the bottlenecks? Nat. Genet. 18 313–318.
2. A.K.Payra and S.Saha, IJCET.2013.Generic approach for predicting unannotated protein pair function using protein.page.142-159
3. Cheng, Chung, Aguan, Yang, Wang, N.Paul, PLoS ONE,2011, Dicovery of protein Phosphorylation Motif through Exploratory Data analysis.
4. Anna R. Panchenko, Fyodor Kondrashov, and Stephen Bryant - Prediction of functional sites by analysis of sequence and structure conservation, 2004Devos, D. and Valencia, A. 2000. Practical limits of function prediction. Proteins 41 98–107.
5. Todd, A.E., Orengo, C.A., and Thornton, J.M. 2001. Evolution of function in protein superfamilies, from a structural perspective. J. Mol. Biol. 307 1113–1143.
6. Casari, G., Sander, C., and Valencia, A. 1995. A method to predict functional residues in proteins. Nat. Struct. Biol. 2 171–178.
7. Andrade, M.A., Casari, G., Sander, C., and Valencia, A. 1997. Classification of protein families and detection of the determinant residues with an improved self-organizing map. Biol. Cybern. 76 441–450.
8. Lichtarge, O., Bourne, H.R., and Cohen, F.E. 1996. An evolutionary trace method defines binding surfaces common to protein families. J. Mol. Biol. 257 342–358.
9. Sjolander, K. 1998. Phylogenetic inference in protein superfamilies: Analysis of SH2 domains. Proc. Int. Conf. Intell. Syst. Mol. Biol. 6 165–174.
10. Aloy, P., Querol, E., Aviles, F.X., and Sternberg, M.J. 2001. Automated structure-based prediction of functional sites in proteins: Applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking. J. Mol. Biol. 311 395–408.
11. Madabushi, S., Yao, H., Marsh, M., Kristensen, D.M., Philippi, A., Sowa, M.E., and Lichtarge, O. 2002. Structural clusters of evolutionary trace residues are statistically significant and common in proteins. J. Mol. Biol. 316 139–154.
13. http://f-motif.classcloud.org 14. “Computational Approaches for Protein Function Prediction: A Survey?- Gaurav Pandey, Vipin Kumar and Michael Steinbach