Abstract
Peptides that bind to Human Leukocyte Antigens (HLA) can be presented to T-cell receptor and trigger immune response. Identification of specific binding peptides is critical for immunology research and vaccine design. However, accurate prediction of peptides binding to HLA molecules is challenging. A variety of methods such as HMM and ANN have been applied to predict peptides that can bind to HLA class I molecules and therefore the number of candidate binders for experimental assay can be largely reduced. However, it is a more complex process to predict peptides that bind to HLA class II molecules. In this paper, we proposed a kernel-based method, integrating the BLOSUM matrix with string kernel to form a new kernel. The substitution score between amino acids in BLOSUM matrix is incorporated into computing the similarity between two binding peptides, which exhibits more biological meaning over traditional string kernels. The promising results of this approach show advantages than other methods.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burden, F.R., Winkler, D.A.: Predictive Bayesian neural network models of MHC class II peptide binding. Journal of Molecular Graphics and Modelling 23, 481–489 (2005)
Bhasin, M., Raghava, G.P.S.: SVM based method for predicting HLA-DRB1(*)0401 binding peptides in an antigen sequence. Bioinformatics 20(3), 421–423 (2004)
Bozic, I., Zhang, G.L., Brusic, V.: Predictive Vaccinology: Optimisation of Predictions Using Support Vector Machine Classifiers. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 375–381. Springer, Heidelberg (2005)
Chicz, R.M., et al.: Specificity and promiscuity among naturally processed peptides bound to HLA-DR alleles. J. Exp. Med. 178(1), 27–47 (1993)
Chen, Y., et al.: Naturally processed peptides longer than nine amino acid residues bind to the class I MHC molecule HLA-A2.1 with high affinity and in different conformations. The journal of immunology 152, 2874–2881 (1994)
Brusic, V., et al.: Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14, 121–130 (1998)
Schueler-Furman, O., et al.: Bound peptides: a study of 23 complexes. Folding design 3, 549–564 (1998)
Froloff, N., Windemuth, A., Honig, B.: On the calculation of binding free energies using continuum methods: Application to MHC class I protein-peptide interactions. Protein Sci. 6(6), 1293–1301 (1997)
Rammensee, H.G., Friede, T., Stevanovic, S.: Mch Ligands And Peptide Motifs - First Listing. Immunogenetics 41(4), 178–228 (1995)
Meister, G.E., et al.: 2 Novel T-Cell Epitope Prediction Algorithms Based On Mhc-Binding Motifs - Comparison Of Predicted And Published Epitopes From Mycobacterium-Tuberculosis And Hiv Protein Sequences. Vaccine 13(6), 581–591 (1995)
Falk, K., et al.: Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature, 290–296 (1991)
Hammer, J., et al.: New Methods To Predict Mhc-Binding Sequences Within Protein Antigens. Current Opinion In Immunology 7(2), 263–269 (1995)
Hammer, J., et al.: Precise Prediction Of Major Histocompatibility Complex Class-Ii Peptide Interaction Based On Peptide Side-Chain Scanning. Journal Of Experimental Medicine 180(6), 2353–2358 (1994)
Mallios, R.R.: Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta- algorithm. Bioinformatics 15(6), 432–439 (1999)
Sturniolo, T., et al.: Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nature Biotechnology 17(6), 555–561 (1999)
Rammensee, H.G., et al.: SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50(3-4), 213–219 (1999)
Mamitsuka, H.M.: Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33, 460–474 (1998)
Donnes, P., Elofsson, A.: Prediction of MHC class I binding peptides, using SVMHC. Bmc Bioinformatics 3(1), 25–32 (2002)
Zhao, Y.D., et al.: Application of support vector machines for T-cell epitopes prediction. Bioinformatics 19(15), 1978–1984 (2003)
Wan, J., et al.: SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics 7(1), 463 (2006)
Brusic, V., et al.: Data Cleansing for Computer Models: A Case Study from Immunology. In: Proceedings of ICONIP99, The Sixth International Conference on Neural Information Processing (1999)
Nielsen, M., et al.: Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20(9), 1388–1397 (2004)
Karpenko, O., Shi, J., Dai, Y.: Prediction of MHC class II binders using the ant colony search strategy. Elsevier Science 35, 147–156 (2005)
Murugan, N., Dai, Y.: Prediction of MHC class II binding peptides based on an iterative learning model. Immunome Research 1(1), 6 (2005)
Doytchinova, I.A., Flower, D.R.: Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction. Bioinformatics 19(17), 2263–2270 (2003)
Yu, H., Zhu, X., Huang, M.: Using String Kernel to Predict Binding Peptides for MHC Molecules. In: The 8th International Conference on Signal Processing, Guilin, China, pp. 2499–2502. IEEE Press, Los Alamitos (2006)
Saigo, H., et al.: Protein homology detection using string alignment kernels. Bioinformatics 20(11), 1682–1689 (2004)
Zhao, X.M., Cheung, Y.M., Huang, D.S.: A novel approach to extracting features from motif content and protein composition for protein sequence classification. Neural Networks 18(8), 1019–1028 (2005)
Mercer, J.: Functions of positive and negative type and their connection with the theory of integral equations. Philos. Trans. Soc. 209, 415–446 (1909)
Lodhi, H., et al.: Text classification using string kernels. Journal Of Machine Learning Research 2(3), 419–444 (2002)
Brown, M.P.S., et al.: Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS 97(1), 262–267 (2000)
Brusic, V., Rudy, G., Harrison, L.C.: MHCPEP, a database of MHC-binding peptides: update 1997. Nucl. Acids Res. 26(1), 368–371 (1998)
Bhasin, M., Singh, H., Raghava, G.P.S.: MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19(5), 665–666 (2003)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Scholkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge (1999)
Max, H., et al.: Characterization of peptides bound to extracellular and intracellular HLA-DR1 molecules. Human immunology 38(3), 193–200 (1993)
Marshall, K.W., et al.: Prediction of peptide affinity to HLA DRB1*0401. J. Immunol. 154(11), 5927–5933 (1995)
Borras-Cuesta, F., et al.: Specific and general HLA-DR binding motifs: comparison of algorithms. Human Immunology 61(3), 266 (2000)
Bhasin, M., Raghava, G.P.S.: Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 22(23-24), 3195–3204 (2004)
Singh, H., Raghava, G.P.S.: ProPred: prediction of HLA-DR binding sites. Bioinformatics 17(12), 1236–1237 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, H., Huang, M., Zhu, X., Guo, Y. (2007). A Novel Kernel-Based Approach for Predicting Binding Peptides for HLA Class II Molecules. In: Măndoiu, I., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science(), vol 4463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72031-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-72031-7_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72030-0
Online ISBN: 978-3-540-72031-7
eBook Packages: Computer ScienceComputer Science (R0)