The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - October-December (2009 vol.6)
pp: 639-651
Pradeep Chowriappa , Louisiana Tech University, Ruston
Sumeet Dua , Louisiana Tech University, Ruston
Jinko Kanno , Louisiana Tech University, Ruston
Hilary W. Thompson , Louisiana State University Health Sciences Center, New Orleans
ABSTRACT
Protein folding is frequently guided by local residue interactions that form clusters in the protein core. The interactions between residue clusters serve as potential nucleation sites in the folding process. Evidence postulates that the residue interactions are governed by the hydrophobic propensities that the residues possess. An array of hydrophobicity scales has been developed to determine the hydrophobic propensities of residues under different environmental conditions. In this work, we propose a graph-theory-based data mining framework to extract and isolate protein structural features that sustain invariance in evolutionary-related proteins, through the integrated analysis of five well-known hydrophobicity scales over the 3D structure of proteins. We hypothesize that proteins of the same homology contain conserved hydrophobic residues and exhibit analogous residue interaction patterns in the folded state. The results obtained demonstrate that discriminatory residue interaction patterns shared among proteins of the same family can be employed for both the structural and the functional annotation of proteins. We obtained on the average 90 percent accuracy in protein classification with a significantly small feature vector compared to previous results in the area. This work presents an elaborate study, as well as validation evidence, to illustrate the efficacy of the method and the correctness of results reported.
INDEX TERMS
Bioinformatics (genome or protein) databases, data mining, hydrophobicity scales, protein folding, structural classification, subgraph mining.
CITATION
Pradeep Chowriappa, Sumeet Dua, Jinko Kanno, Hilary W. Thompson, "Protein Structure Classification Based on Conserved Hydrophobic Residues", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.6, no. 4, pp. 639-651, October-December 2009, doi:10.1109/TCBB.2008.77
REFERENCES
[1] C.S. Leslie, E. Eskin, A. Cohen, J. Weston, and W.S. Noble, “Mismatch String Kernels for Discriminative Protein Classification,” Bioinformatics, vol. 20, no. 4, pp. 467-476, 2004.
[2] A. Shmygelska, “Search for Folding Nuclei in Native Protein Structures,” Bioinformatics, vol. 21, no. 1, pp. 394-402, 2005.
[3] W.Z. Kauzmann, “Factors in the Interpretation of Protein Denaturation,” Advanced Protein Chemistry, vol. 14, pp. 1-63, 1959.
[4] A. Paiardini, F. Bossa, and S. Pascarella, “Evolutionarily Conserved Regions and Hydrophobic Contacts at the Superfamily Level: The Case of the Fold-Type I, Pyridoxal-5'-Phosphate-Dependent Enzymes,” Protein Sciences, vol. 13, pp. 2992-3005, 2004.
[5] B.V.B. Reddy, W.W. Li, I.N. Shindyalov, and P.E. Bourne, “Conserved Key Amino Acid Positions (CKAAPs) Derived from the Analysis of Common Substructures in Proteins,” PROTEINS: Structure, Function, and Genetics, vol. 42, pp. 148-163, 2001.
[6] C.-J. Tsai and R. Nussinov, “Hydrophobic Folding Units Derived from Dissimilar Monomer Structures and Their Interactions,” Protein Science, vol. 6, no. 1, pp. 24-42, 1997.
[7] U.K. Muppirala and Z. Li, “A Simple Approach for Protein Structure Discrimination Based on the Network Pattern of Conserved Hydrophobic Residues,” Protein Eng., Design, and Selection, vol. 19, no. 6, pp. 265-275, 2006.
[8] E.S. Huang, S. Subbiah, and M. Levitt, “Recognizing Native Folds by the Arrangement of Hydrophobic and Polar Residues,” J.Molecular Biology, vol. 252, pp. 709-720, 1995.
[9] K.M. Biswas, D.R. DeVido, and J.G. Dorsey, “Evaluation of Methods for Measuring Amino Acid Hydrophobicities and Interactions,” J. Chromatography A, vol. 1000, no. 1, pp. 637-655, 2003.
[10] J.L. Cornette, K.B. Cease, H. Margalit, J.L. Spounge, J.A. Berzofsky, and C. DeLisi, “Hydrophobicity Scales and Computational Techniques for Detecting Amphipathic Structures in Proteins,” J.Molecular Biology, vol. 195, no. 3, pp. 659-685, 1987.
[11] M.C.J. Wilce, M.-I. Aguilar, and M.T. Hearn, “Physicochemical Basis of Amino Acid Hydrophobicity Scales: Evaluation of Four New Scales of Amino Acid Hydrophobicity Coefficients Derived from RP-HPLC of Peptides,” Analytical Chemistry, vol. 67, no. 7, pp. 1210-1219, 1995.
[12] H. Hu, X. Yan, Y. Huang, J. Han, and X.J. Zhou, “Mining Coherent Dense Subgraphs across Massive Biological Networks for Functional Discovery,” Bioinformatics, vol. 21, pp. i213-i221, suppl. 1, 2005.
[13] B. Krishnamoorthy and A. Torpsha, “Development of a Four-Body Statistical Pseudo-Potential to Discriminate Native from Non-Native Protein Conformations,” Bioinformatics, vol. 19, no. 12, pp. 1540-1548, 2003.
[14] J. Huan, W. Wang, D. Bandyopadhyay, J. Snoeyink, J. Prins, and A. Tropsha, “Mining Protein Family Specific Residue Packing Patterns from Protein Structure Graphs,” Proc. Eighth Ann. Int'l Conf. Computational Molecular Biology (RECOMB '04), Mar. 2004.
[15] Z. Bagci, R.L. Jernigan, and I. Bahar, “Residue Packing in Proteins: Uniform Distribution on a Coarse-Grained Scale,” J. Chemical Physics, vol. 116, no. 5, pp. 2269-2276, 2002.
[16] T.J. Taylor and I.I. Vaisman, “Graph Theoretic Properties of Networks Formed by the Delaunay Tessellation of Protein Structures,” Physical Rev. E, vol. 73, no. 4, pp. 041925-1-041925-13, Apr. 2006.
[17] R. Tarjan, “Depth-First Search and Linear Graph Algorithms,” SIAM J. Computing, vol. 1, no. 2, pp. 146-160, 1972.
[18] J.-C. Gelly, A.G. de Brevern, and S. Hazout, “”Protein Peeling”: An Approach for Splitting a 3D Protein Structure into Compact Fragments,” Bioinformatics, vol. 22, no. 2, pp. 129-133, 2006.
[19] K. Nishikawa and T. Ooi, “Prediction of the Surface Interior Diagram of Globular Proteins by an Empirical Method,” Int'l J. Peptide and Protein Research, vol. 16, no. 1, pp. 19-32, 1980.
[20] B. Lee and F.M. Richards, “The Interpretation of Protein Structures: Estimation of Static Accessibility,” J. Molecular Biology, vol. 55, no. 3, pp. 379-400, 1971.
[21] L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5-32, Oct. 2001.
[22] A. Sacan, O. Ozturk, H. Ferhatosmanoglu, and Y. Wang, “LFM-Pro: A Tool for Detecting Significant Local Structural Sites in Proteins,” Bioinformatics, vol. 23, no. 6, pp. 709-716, 2007.
[23] E.W. Stawiski, A.E. Baucom, S.C. Lohr, and L.M. Gregoret, “Predicting Protein Function from Structure: Unique Structural Features of Proteases,” Proc. Nat'l Academy of Sciences USA, vol. 97, no. 8, p. 3954, 2000.
25 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool