Predicting and Analyzing Lipid-Binding Proteins Using an Efficient Physicochemical Property Mining Method

Abstract:

Lipid-binding proteinsjoin many important biological processes. Lipid-binding proteins are highly related to diseases, such as metabolic diseases, cancer and autoimmune diseases. The existed studies of predictinglipid-binding functions or predictinglipid-binding sites, but notidentify the lipid-binding proteins ornot lipid-binding proteins.This study purpose a systematic approach to identify a small set of physicochemical and biochemical properties in AAindex database to design support vector machine (SVM) based classifier for predicting and analyzing lipid-binding proteins. The merits of this study are three-fold: First, we establish a data set of lipid-binding proteins collected from SwissProt utilizing the gene ontology (GO) annotation terms. Secondly, utilize an efficient genetic algorithm based optimization method IBCGA to select an informative set of feature vectors of representing sequences from the viewpoint of machine learning. Thirdly, analyze the selected feature vectors to identify the related physicochemical properties which may affect the binding mechanism oflipid-binding proteins. In this study, to overcome the unbalanced dataset problem caused from the number of putative negative dataset (537,346) being almost 530 times to that of positive dataset (1,053), a dataset determining technique is proposed.Then the dataset is applied to make a high performance classifier. The prediction accuracy of independent test is 77.75% using 18 properties. The selected 18 properties may divide into 6 groupings:alpha and turn propensities, beta propensity, Composition, Hydrophobicity, Physicochemical properties and other properties.Hydrophobicity and alpha-helix are most relative to lipid-binding protein.

• Periodical: Applied Mechanics and Materials (Volume 421)
• Pages: 313-318
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.421.313
• Online since: September 2013
• Authors: Hui Ling Huang, Yi Fan Liou, Hua Chin Lee, Phasit Charoenkwan, Chao Di Chang, Yerukala Sathipati Srinivasulu, Wen Liang Chen, Tamara Vasylenkoand, Shinn Ying Ho
• Keywords: Genetic Algorithm (GA), Lipid-Binding Proteins, Physicochemical Properties, Prediction, SVM

References

[1] Downes CP, Gray A, Lucocq JM: Probing phosphoinositide functions in signaling and membrane trafficking. Trends Cell Biol 2005, 15(5): 259.

DOI: 10.1016/j.tcb.2005.03.008

[2] Cho W, Stahelin RV: Membrane-protein interactions in cell signaling and membrane trafficking. Annual review of biophysics and biomolecular structure 2005, 34: 119.

DOI: 10.1146/annurev.biophys.33.110502.133337

[3] Furuhashi M, Hotamisligil GS: Fatty acid-binding proteins: role in metabolic diseases and potential as drug targets. Nat Rev Drug Discov 2008, 7(6): 489.

DOI: 10.1038/nrd2589

[4] Wymann MP, Schneiter R: Lipid signalling in disease. Nature reviews Molecular cell biology 2008, 9(2): 162.

DOI: 10.1038/nrm2335

[5] Glatz JFC, Luiken JJFP, van Bilsen M, van der Vusse GJ: Cellular lipid binding proteins as facilitators and regulators of lipid metabolism. Mol Cell Biochem 2002, 239(1-2): 3.

DOI: 10.1007/978-1-4419-9270-3_1

[6] Gallego-Ortega D, Ramirez de Molina A, Ramos MA, Valdes-Mora F, Barderas MG, Sarmentero-Estrada J, Lacal JC: Differential role of human choline kinase alpha and beta enzymes in lipid metabolism: implications in cancer onset and treatment. PloS one 2009, 4(11): e7819.

DOI: 10.1371/journal.pone.0007819

[7] Hilvo M, Denkert C, Lehtinen L, Muller B, Brockmoller S, Seppanen-Laakso T, Budczies J, Bucher E, Yetukuri L, Castillo S et al: Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression. Cancer Res 2011, 71(9): 3236.

DOI: 10.1158/0008-5472.can-10-3894

[8] Bunney TD, Katan M: Phosphoinositide signalling in cancer: beyond PI3K and PTEN. Nat Rev Cancer 2010, 10(5): 342.

DOI: 10.1038/nrc2842

[9] Beagle B, Fruman DA: A Lipid Kinase Cousin Cooperates to Promote Cancer. Cancer Cell 2011, 19(6): 693.

DOI: 10.1016/j.ccr.2011.05.020

[10] Fruman DA, Bismuth G: Fine tuning the immune response with PI3K. Immunol Rev 2009, 228: 253.

DOI: 10.1111/j.1600-065x.2008.00750.x

[11] Lin HH, Han LY, Zhang HL, Zheng CJ, Xie B, Chen YZ: Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity. J Lipid Res 2006, 47(4): 824.

DOI: 10.1194/jlr.m500530-jlr200

[12] Xiong WJ, Guo YZ, Li ML: Prediction of Lipid-Binding Sites Based on Support Vector Machine and Position Specific Scoring Matrix. Protein J 2010, 29(6): 427.

DOI: 10.1007/s10930-010-9269-x

[13] Kawashima S, Ogata H, Kanehisa M: AAindex: Amino Acid Index Database. Nucleic Acids Res 1999, 27(1): 368.

DOI: 10.1093/nar/27.1.368

[14] Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics (Oxford, England) 2010, 26(19): 2460.

DOI: 10.1093/bioinformatics/btq461

[15] Chang C-CuaL, C. -J.: LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology. ACM Transactions on Intelligent Systems and Technology 2011, 2(3): 27: 1.

DOI: 10.1145/1961189.1961199

[16] Ho SY, Chen JH, Huang MH: Inheritable genetic algorithm for biobjective 0/1 combinatorial optimization problems and its applications. IEEE Trans Syst Man Cybern B Cybern 2004, 34(1): 609.

DOI: 10.1109/tsmcb.2003.817090

[17] Tung CW, Ho SY: POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties. Bioinformatics (Oxford, England) 2007, 23(8): 942.

DOI: 10.1093/bioinformatics/btm061

[18] Marion D, Bakan B, Elmorjani K: Plant lipid binding proteins: Properties and applications. Biotechnol Adv 2007, 25(2): 195.

DOI: 10.1016/j.biotechadv.2006.11.003