Table 5. Theoretical prediction of HER-2 epitopes. Amino acids 11–14, 17–19, 22 42, 44–48, 50, 53 66, 68–71, 75 87–89, 96, 98–101, 113–115, 1.7 117 135–139 155, 157–158, 160–173, 175– 1.25 177, 179, 181 186–189 251–253, 259–265 295–297, 301–307 311–319 327–328, 330–333 0.85 1.13 0.65 0.96 0.42 1.67 1.59 1.12 0.89 Maximal potential* Amino acids Maximal potential* 337–338, 344–345, 0.72 347 357–358, 360–375 1.55 377–378, 380–381 0.5 395–399 0.9 462–463, 465–468 0.78 475–476, 481–482, 0.64 485–487 526–530, 535–536 0.9 577–578, 580–601 1.74 549–552 603–608 1.13 1.3 Epitope Mapping with Mimotopes non-fat milk in TBS and probed with depleted mouse sera (1:1000 in TBS/0.5% non-fat milk). Bound antibodies were detected using goat anti-mouse IgG conjugated with HRP and the ECL chemiluminescent reagent. DNAs from the serum-specific phages were subsequently purified and the pVIII inserts were sequenced and translated to amino acids for further analysis. Computer algorithm Amino acid sequences of affinity-selected phages were used as a database for the computer algorithm [9,11] to perform a cluster analysis. The algorithm is based on the assumption that affinity- selected peptides must share key contact residues with the true epitopes in the native protein. In the three-dimensional structure of a protein, amino acids creating the epitope can be discontinuous and become juxtaposed at the surface of the antigen through protein folding. Therefore, we assume that neighboring amino acids within the selected phage-displayed peptides may represent pairs of juxtaposed amino acids residues on the protein surface. All conserved residues were consolidated into six functional subgroups of amino acids and given single-letter codes: Arg, Lys = B; Glu, Asp = J; Ser, Thr = O; Leu, Ile, Val = U; Asn, Gln = X; Trp, Phe = Z. Additionally, we assume that the most frequently observed tandem amino acid pairs within a given set of affinity- selected peptides likely represent the contact amino acids within the epitope. The computer algorithm was used to find clusters of these pairs on a surface of HER-2 protein by analyzing its three- dimensional model. Three-dimensional models of HER-2 were created by RasMol software (http://www.bernstein-plus-sons. com/software/rasmol/doc/rasmol.html). To corroborate the predictive power of our strategy, we also employed the DiscoTope software [41] which predicts antibody epitopes based on ‘‘a combination of amino acid statistics, spatial information, and surface exposure’’. It is created on the basis of a compiled data set from 76 X-ray structures of antibody/antigen protein complexes. Author Contributions Conceived and designed the experiments: GD DD SRF JB. Performed the experiments: GD CE MW JB. Analyzed the data: GD DD JB. Wrote the paper: GD JB. 9. Denisova GF, Denisov DA, Yeung J, Loeb MB, Diamond MS, et al. (2008) A novel computer algorithm improves antibody epitope prediction using affinity- selected mimotopes: A case study using monoclonal antibodies against the West Nile virus E protein. Mol Immunol 46: 125–134. 10. Denisova GF, Zerwanitzer M, Denisov DA, Spectorman E, Mondor I, et al. (2000) Expansion of epitope cross-reactivity by anti-idiotype modulation of the primary humoral response. Mol Immunol 37: 53–58. 11. Enshell-Seijffers D, Denisov D, Groisman B, Smelyanski L, Meyuhas R, et al. (2003) The mapping and reconstitution of a conformational discontinuous B-cell epitope of HIV-1. J Mol Biol 334: 87–101. 12. Gershoni JM, Stern B, Denisova G (1997) Combinatorial libraries, epitope structure and the prediction of protein conformations. Immunol Today 18: 108–110. 13. Paley EL, Smelyanski L, Malinovskii V, Subbarayan PR, Berdichevsky Y, et al. (2007) Mapping and molecular characterization of novel monoclonal antibodies to conformational epitopes on NH2 and COOH termini of mammalian tryptophanyl-tRNA synthetase reveal link of the epitopes to aggregation and Alzheimer’s disease. Mol Immunol 44: 541–557. 14. Stern B, Denisova G, Buyaner D, Raviv D, Gershoni JM (1997) Helical epitopes determined by low-stringency antibody screening of a combinatorial peptide library. FASEB J 11: 147–153. 15. Denisov DA, Denisova GF, Lelic A, Loeb MB, Bramson JL (2009) Deciphering epitope specificities within polyserum using affinity selection of random peptides and a novel algorithm based on pattern recognition theory. Mol Immunol 46: 429–436. *potential calculated for every amino acid using DiscoTope software and characterizing immunogenic property of a given amino acid. doi:10.1371/journal.pone.0005309.t005 where X represents any amino acid (kindly provided by Dr. J. Scott, Simon Fraser University) [38]. Screening of the phage library was performed according to the method described by Smith [39]. The library was initially depleted of phages recognized by na¨ıve mouse serum by 3 sequential pannings of the library with immobilized serum of non-immunized mice. The resultant ‘‘depleted’’ library was used for mimotope identification. Similarly, the sera obtained from immunized mice were initially depleted of anti- phage activity by overnight incubation at 4uC with wild type phage immobilized on plastic plates. The sera were also depleted of IgM by similar incubation with immobilized anti-IgM antibody. Serum specific peptides were selected by two rounds of iterative affinity selection and specificity of phages for different mouse sera was assessed by dot blot (according to the method described in [40]). Briefly, phages were immobilized onto nitrocellulose membranes. The membranes were then blocked with 5% References 1. Slamon DJ, Godolphin W, Jones LA, Holt JA, Wong SG, et al. 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