Identification of Outer Membrane Proteins Utilizing K-Nearest Neighbor

Maqsood Hayat, Mohammad Sohail, Haroon Khan, Muhammad Noman Hayat

Abstract


Outer Membrane Proteins (OMPs) assume essential part in cell science. The separation of OMPs from genomic groupings is a testing assignment because of short layer spreading over areas with high variety in properties. Subsequently, a mechanized and high throughput computational model for separation of OMPs from their essential groupings is required. In this paper, we have used K-closest Neighbor in mix with Amino corrosive piece. The execution of K-closest Neighbor s is assessed by two datasets utilizing 5-fold cross-approval. After the test, we have watched that K-closest Neighbor makes the most elevated progress rate of 96.0% exactness for segregating OMPs from non-OMPs and 96.3% and 96.5% correctnesses from α-helix film and Globular proteins, separately on dataset1. While on dataset2, K-closest Neighbor acquires 96.4% exactness for separating OMPs from non-OMPs.


References


Bagos, P.G., Liakopoulos, T.D., Spyropoulos, I.C., and Hamodrakas, S.J., 2004a. PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins. Nucleic Acids Res. 1(32), web server issue

Bagos, P.G., Liakopoulos, T.D., Spyropoulos, I.C., and Hamodrakas, S.J., 2004b. A Hidden Markov Model Method, Capable of Predicting and Discriminating β-Barrel Outer Membrane Proteins, BMC Bioinformatics 5(29)

Berven, F.S., Flikka, K., Jensen, H.B., Eidhammer, I., 2004. BOMP: a program to predict integral beta-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria. Nucleic Acids Res. 32 Web Server: W394-399

Bigelow H, Rost B., 2006. PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins. Nucleic Acids Res. 34 Web Server: W186-188.

Chou, J.J., Zhang, C.T., 1993. A joint prediction of the folding types of 1490 Human proteins from their Genetic Codons. J. Theoretical Biology 161, 251-262.

Chou, K.C., Zhang C.T., 1994. Predicting protein folding types by distance functions that make allowances for amino acid interactions. J Biol Chem. 269(35), 22014-22020.

Chou, K.C., 1995. A novel approach to predicting protein structural classes in a (20–1)-D amino acid composition space. Proteins 21, 319–344

Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics 43, 246-255.

Chou, K.C., Shen, H.B., 2006a. Hum-PLoc: a novel ensemble classifier for predicting human protein Subcellular localization. Biochem Biophys Res Commun. 347, 150–157.

Chou, K.C., Shen, H.B., 2006b. Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers. J Proteome Res.,5, 1888–1897.

Chou, K.C., and Y.D. Cai, Y.D., 2005. Prediction of membrane protein types by incorporating amphipathic effects. J. Chem. Inf. Model. 45, pp 407-413

Duda, R.O., Hart, P.E., and Stork, D.G., 2000. Pattern Classification, 2nd edn.Wiley, NewYork.

Debnath, R., Kurita, T., 2010. An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theories. BioSystems 100, 39–46

Espejo, P.G., Ventura, S., Herrera, F., 2010. A Survey on the Application of Genetic Programming to Classification, IEEE Transactions on Systems, Man, And Cybernetics, part C 40(2), Pp 121-144.

Faraoun, K.M., and Boukelif, A., 2006. Genetic Programming: Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection. International Journal of Computational Intelligence Vol. 3, No. 1, 79-90

Gromiha, M.M., Suwa, M., 2006. Discrimination of outer membrane proteins using machine learning algorithms. Proteins 63, 1031–1037.

Gnanasekaran, T.V., Peri, S., Arockiasamy, A., Krishnaswamy, S., 2000. Profiles from structure based sequence alignment of porins can identify β-stranded integral membrane proteins. Bioinformatics 16, 839–842.

Gromiha, M.M., Suwa, M., 2005. A simple statistical method for discriminating outer Membrane proteins with better accuracy. Bioinformatics 21, 961–968

Gao, Q.B., Ye, X.F., Jin, Z.C., He, J., 2010. Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition. Analytical Biochemistry 398, 52–59.

Hayat, M., and Khan, A., 2011. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. Journal of Theoretical Biology Volume 271, Issue 1, 21, Pp 10-17.

Huang, Y., and Li, Y., 2004. Prediction of Protein Subcellular Locations Using Fuzzy k- NN Method. Bioinformatics vol. 20, pp. 21-28

Huang, H.L., Chang, F.L., 2007. ESVM:Evolutionary support vector machine for automatic feature selection and classification of microarray data. BioSystems 90, 516–528

Hue, M., Riffle, M., Vert, J.P., Noble, W.S., 2010. Large-scale prediction of protein-protein interactions from structures. BMC Bioinformatics, 11:144

Khan, A., Khan, M.K., and Choi, T.S., 2008. Proximity Based GPCRs Prediction in Transform Domain, Biochemical and Biophysical Research Communications 24, 371(3) pp.411-415

Keller, J.M., Gray, M.R., and Givens, J.A., 1995. A fuzzy k-nearest neighbour algorithm. IEEE Trans. Syst. Man Cybern. 15, 580–585.

Khan, A., Majid, A., and Choi, T.S., 2010. Predicting Protein Subcellular Location: Exploiting Amino Acid Based Sequence of Feature Spaces and Fusion of Diverse Classifiers. Amino Acids 38: 347-350

Lin, H., 2008. The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition. Journal of Theoretical Biology. 252, 350–356.

Lei, Z., Dai, Y., 2004. A novel approach for prediction of protein subcellular localization from sequence using Fourier analysis and support vector machines; Proceedings of 4th ACM SIGKDD Workshop on Data Mining in Bioinformatics, p11-17

Liang, G.Z., Ma, X.Y., Li, Y.C., Lv, F.L., Yang, L., 2010. Toward an improved discrimination of outer membrane proteins using a sequence-based approach. Biosystems 2011, in press.

Mizianty, M.J., Kurgan, L., 2011. Improved identification of outer membrane beta barrel proteins using primary sequence, predicted secondary structure, and evolutionary information. Proteins Structure Function, Bioinformatics 79, 294-303.

Nanni, L., and Lumini, A., 2008. Genetic programming for creating Chou’s Pseudo amino acid based features for submitochondria localization. Amino Acids 34, 653-660

Ou, Y.Y., Gromiha, M.M., Chen, S.A., Suwa, M., 2008. TMBETADISC-RBF: discrimination of b-barrel membrane proteins using RBF networks and PSSM profiles. Comput. Biol. Chem. 32, 227–231.

Park, K.J., Gromiha, M.M., Horton, P., Suwa, M., 2005. Discrimination of outer Membrane proteins using support vector machines, Bioinformatics 21, 4223–4229.

Remmert, M., Linke, D., Lupas, A.N., Soding, J., 2009. HHomp: prediction and classification of outer membrane proteins. Nucleic Acids Res, 37 Web Server: W446-451.

Shen, H.S., Chou, K.C., 2008. PseAAC: a flexible web-server for generating various kinds of protein pseudo amino acid composition. Analytical Biochemistry 373, 386-388.

Sim, J., Kim, S.Y., Lee, J., 2005. Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method. Bioinformatics 21(12), 2844-2849.

Shen, H.S., Yang, J., and Chou, K.C., 2006. Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. J Theor. Biol. 240(1), pp 9-13

Smart, W., and Zhang, M., 2003. Classification strategies for image classification in genetic programming. Proceeding of Image and Vision Computing conference, Pp 402-407.

Sugimoto, M., Kikuchi, S., Tomita, M., Reverse engineering of biochemical equations from time-course data by means of genetic programming. BioSystems 80, 155–164.

Verma, R., Varshney, G.C., Raghava, G.P.S., 2010. Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile. Amino Acids 39: 101-110.

Wimley, W.C., 2002. Toward genomic identification of beta-barrel membrane proteins: composition and architecture of known structures. Protein Sci. 11,301–312

Wu, Z., Feng, E., Wang, Y., Chen, L., 2007. Discrimination of outer membrane proteins by a new measure of information discrepancy. Protein Pept. Lett. 14, 34–44.

Yan, C., Hu, J., Wang, Y., 2008. Discrimination of outer membrane proteins with improved performance. BMC Bioinformatics 9 (47).

Yan, C., Hu, J., Wang, Y., 2008. Discrimination of outer membrane proteins using a k-nearest neighbor method, Amino Acids 35, 65–73.

Zhai, Y., Saier, M.H., 2002. The β-barrel finder (BBF) program, allowing identification of outer membrane β-barrel proteins encoded within prokaryotic genomes. Protein Sci. 11, 2196–2207.


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