Abstract
The identification of MHC class-II restricted epitope is an important goal in peptide based vaccine and diagnostic development. In the present study, we discuss the applications of Gibbs motif sampler, weight matrix and artificial neural network for the prediction of peptide binding to sixteen MHC class-II molecules of human and mouse. The average prediction performances of sixteen MHC class-II molecules in terms of Aroc, based on Gibbs motif sampler, sequence weighting and artificial neural network are 0.56, 0.55 and 0.51 respectively. However, further improvements in the performance of software tools for prediction of MHC class-II binding peptide based on various methods largely depends on the size of training and validation datasets and the correct identification of the peptide binding core.
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References
Cresswell, P.: Assembly, transport, and function of MHC class II molecules. Annu. Rev. Immunol. 12, 259–293 (1994)
Peters, B., Sette, A.: Integrating epitope data into the emerging web of biomedical knowledge resources. Nat. Rev. Immunol. 7, 485–490 (2007)
Stern, L.J., Brown, J.H., Jardetzky, T.S., Gorga, J.C., Urban, R.G., et al.: Crystal structure of the human class II MHC protein HLA-DR1 complexed with an influenza virus peptide. Nature 368, 215–221 (1994)
Jones, E.Y., Fugger, L., Strominger, J.L., Siebold, C.: MHC class II proteins and disease: a structural perspective. Nat. Rev. Immunol. 6, 271–282 (2006)
Godkin, A.J., Smith, K.J., Willis, A., Tejada-Simon, M.V., Zhang, J., et al.: Naturally processed HLA class II peptides reveal highly conserved immunogenic flanking region sequence preferences that reflect antigen processing rather than peptide-MHC interactions. J. Immunol. 166, 6720–6727 (2001)
Rammensee, H., Bachmann, J., Emmerich, N., Bachor, O., Stevanovic, S.: SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219 (1999)
Bhasin, M., Singh, H., Raghava, G.P.: MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19, 665–666 (2003)
Toseland, C.P., Clayton, D.J., McSparron, H., Hemsley, S.L., Blythe, M.J., et al.: AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res. 1, 4 (2005)
Reche, P.A., Zhang, H., Glutting, J.-P., Reinherz, E.L.: EPIMHC: a curated database of MHC-binding peptides for customized computational vaccinology. Bioinformatics 21(9), 2140–2141 (2005)
Peters, B., Sidney, J., Bourne, P., Bui, H.H., Buus, S., et al.: The immune epitope database and analysis resource: from vision to blueprint. PLoS Biol. 3, 91 (2005)
Hammer, J., Bono, E., Gallazzi, F., Belunis, C., Nagy, Z., et al.: Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning. J. Exp. Med. 180, 2353–2358 (1994)
Reche, P.A., Glutting, J.-P., Reinherz, E.L.: Prediction of MHC Class I Binding Peptides Using Profile Motifs. Human Immunology 63, 701–709 (2002)
Mamitsuka, H.: Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33(4), 460–474 (1998)
Donnes, P., Elofsson, A.: Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics 3(1), 25 (2002)
Honeyman, M.C., Brusic, V., Stone, N.L., Harrison, L.C.: Neural network-based prediction of candidate T-cell epitopes. Nat. Biotechnol. 16, 966–969 (1998)
Bui, H.H., Sidney, J., Peters, B., Sathiamurthy, M., Sinichi, A., et al.: Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 57, 304–314 (2005)
Nielsen, M., Lundegaard, C., Lund, O.: Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 8, 238 (2007)
Nielsen, M., Lundegaard, C., Worning, P., Hvid, C.S., Lamberth, K., Buus, S., Brunak, S., Lund, O.: Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20, 1388–1397 (2004)
Henikoff, S., Henikoff, J.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89, 10915–10919 (1992)
Nielsen, M., Lundegaard, C., Worning, P., Lauemøller, S.L., Lamberth, K., Buus, S., Brunak, S., Lund, O.: Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12(5), 1007–1017 (2003)
Wang, P., Sidney, J., Dow, C., Mothé, B., Sette, A., et al.: A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach. PLoS Comput. Biol. 4(4), e1000048 (2008)
Hobohm, U., Scharf, M., Schneider, R., Sander, C.: Selection of representative protein data sets. Protein Sci. 1(3), 409–417 (1992)
Singh, S.P., Mishra, B.N.: Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network. Bioinformation 3(4), 150–155 (2008)
Singh, S.P., Mishra, B.N.: Ranking of binding and nonbinding peptides to MHC class–I molecules using inverse folding approach: Implications for vaccine design. Bioinformation 3(2), 72–82 (2008)
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Singh, S.P., Mishra, B.N. (2009). Gibbs Motif Sampler, Weight Matrix and Artificial Neural Network for the Prediction of MHC Class-II Binding Peptides. In: Ranka, S., et al. Contemporary Computing. IC3 2009. Communications in Computer and Information Science, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_48
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DOI: https://doi.org/10.1007/978-3-642-03547-0_48
Publisher Name: Springer, Berlin, Heidelberg
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