Abstract
Multi-Objective Evolutionary Algorithms (MOEA) use Genetic Algorithms (GA) to find a set of potential solutions, which are reached by compromising trade-offs between the multiple objectives. This paper presents a novel approach using MOEA to search for a motif which can unravel rules governing peptide binding to medically important receptors with applications to drugs and vaccines target discovery. However, the degeneracy of motifs due to the varying physicochemical properties at the binding sites across large number of active peptides poses a challenge for the detection of motifs of specific molecules such as MHC Class II molecule I-Ag7 of the non-obese diabetic (NOD) mouse. Several motifs have been experimentally derived for I-Ag7 molecule, but they differ from each other significantly. We have formulated the problem of finding a consensus motif for I-Ag7by using MOEA as an outcome that satisfies two objectives: extract prior information by minimizing the distance between the experimentally derived motifs and the resulting matrix by MOEA; minimize the overall number of false positives and negatives resulting by using the putative MOEA-derived motif. The MOEA results in a Pareto optimal set of motifs from which the best motif is chosen by the Area under the Receiver Operator Characteristics (AROC) performance on an independent test dataset. We compared the MOEA-derived motif with the experimentally derived motifs and motifs derived by computational techniques such as MEME, RANKPEP, and Gibbs Motif Sampler. The overall predictive performance of the MOEA derived motif is comparable or better than the experimentally derived motifs and is better than the computationally derived motifs.
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References
Deb, K., et al.: A Fast and Elitist Multiobjective Genetic Algorithm. IEEE Trans. on Evolutionary Computation 6, 182–197 (2002)
Zitzler, E., et al.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength of Pareto Approach. IEEE Trans. on Evolutionary Computation 3, 257–271 (1999)
Knowles, J.D., et al.: Approximating the Nondominant front using the Pareto Archived evolution stratergy. Evolutionary Computation 8, 49–172 (2000)
Fonseca, C.M., et al.: Genetic Algorithms for Multiobjective Optimization: Formulation, discussion and generalization. In: Forrest, S. (ed.) Proc. of the fifth Intl. conference on Genetic Algorithms, pp. 416–423. Morgan Kauffman, San Mateo (1993)
Lee, S., et al.: Comparison of Multi-Objective Genetic Algorithms in Optimizing Q-Law- Thrust Orbit Transfers. In: Lee, S., et al. (eds.) GECCO (2005)
Amor, S., et al.: Encephalitogenic epitopes of myelin basic protein, proteolipid proteing, and myelin oligodendrocyte glycoprotein for experimental allergic en-cephalomyelitis induction in Biozzi AB/H(H-2Ag 7) mice share an amino acid motif. J. Immunology 156, 3000–3008 (1996)
Reich, E.P., et al.: Self peptides isolated from MHC glycoproteins of non-obese diabetic mice. J. Immunology 152, 2279–2288 (1994)
Rammensee, H., et al.: SYFPEITHI:database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219 (1999)
Reizis, B., et al.: Molecular characterization of the diabetes mouse MHC class-II protein, IAg 7. Int. Immunology 9, 43–51 (1997)
Harrison, L.C., et al.: A peptide binding motif for I- Ag 7, the class II major jistocompatibility complex (MHC) molecule of NOD and Biozzi AB/H mice. J. Exp. Med. 185, 1013–1021 (1997)
Latek, R.R., et al.: Structural basis of peptide binding and presentation by the type I diabetes- associated MHC class II molecule of NOD mice. Immunity 12, 699–710 (2000)
Gregori, S.: The motif for peptide binding to the insulin-dependent diabetes mellitusassociated class II MHC molecule I-Ag 7 validated by phage display library. Int. Immunology 12(4), 493–503 (2000)
Corper, A.L., et al.: A structural framework for deciphering the link between I-Ag 7 and autoimmune diabetes. Science 288, 505–511 (2000)
Yu, B., et al.: Binding of conserved islet peptides by human and murine MHC class II molecules associated with susceptibility to type I diabetes. J. Immunology 30(9), 2497–2506
Suri, A., et al.: In APCs, the Autologous Peptides Selected by the Diabetogenic I-Ag 7 Molecule Are Unique and Determined by the Amino Acid Changes in the P9 Pocket. J. Immunol. 168(3), 1235–1243 (2002)
Stratman, T., et al.: The I-Ag 7 MHC class II molecule linked to murine diabetes in a promiscuous peptide binder. J. Immunology 165, 3214–3225 (2000)
Brusic, V.: An unpublished dataset
Brusic, V., Rudy, G., Harrison, L.C.: MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res 26, 368–371 (1998)
Bailey, T.L., Elkan, C.: The value of prior knowledge in discovering motifs with MEME. In: Proc. Int. Conf. Intell. Syst. Mol. Biol., vol. 3, pp. 21–29 (1995)
Péer, I., et al.: Proteomic Signatures:Amino Acid and Oligopeptide Compositions Differentiate Among Phyla. Proteins 54, 20–40 (2004)
Neuwald, A.F., et al.: Gibbs motif sampling: detection of bacterial outer membrane protein repeats. Protein Science 4, 1618–1632 (1995)
Reche, P.A., et al.: Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 56, 405–419 (2004)
Coello, C.A.: An Updated Survey of Evolutionary Multiobjective Optimization Techniques: State of the Art and Future Trends. In: Congress on Evolutionary Computation, pp. 3–13. IEEE Service Center, Washington (1999)
Carraso-Marin, E., Kanagawa, O., Unanue, E.R.: The lack of consensus for I-Ag7-peptide binding motifs: Is there a requirement for anchor amino acid side chain. Proc. Natl. Acad. Sci. 96, 8621–8626 (1999)
Rajapakse, M., Wyse, L., Schmidt, B., Brusic, V.: Deriving Matrix of Peptide-MHC Interactions in Diabetic Mouse by Genetic Algorithm. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 440–447. Springer, Heidelberg (2005)
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Rajapakse, M., Schmidt, B., Brusic, V. (2006). Multi-Objective Evolutionary Algorithm for Discovering Peptide Binding Motifs. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_14
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DOI: https://doi.org/10.1007/11732242_14
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