Otein. For the PAP4 serum that did not produce significant matches to the PAP protein by BLAST analysis, all three motifs were represented equally. We also used MEME software to analyze the sequences of Title Loaded From File proteins that had been selected as the candidate antigens for the PAP1, PAP2 and the PAP3 sera based on their higher final score compared to the PAP isoforms. The MEME analysis identified the same motifs related to the NFTLPSWA and the QHEPYPL sequences of the PAP protein (Figure 3), suggesting that the PAP1, PAP2 and PAP3 sera could cross-react with these proteins. We also analyzed the PAP protein sequence using available online tool for linear epitope prediction http://sysbio.unl.edu/ SVMTriP/prediction.php. The software based on the Support Vector Machine algorithm predicted existence of three linear epitopes within the PAP sequence (Table 2). Although the NFTLPSWA sequence was not included in any of the predicted epitopes, the epitope predicted with the highest score included the QHEPYPL sequence recognized by the PAP3 antiserum. Anotherpredicted epitope contained the match to the peptide NTTNSHG from the PAP3 list, which retrieved the PAP isoforms by the BLAST searching of the peptide sequence against human refseq_protein database.Validating the SAS Results of Mouse Sera Profiling Using the Anti-peptide ELISATo prove 16985061 that the sequences identified by the SAS method represent the real linear epitopes recognized by serum antibodies, we analyzed PAP-specific antisera by ELISA using peptide library consisting of ML 281 site 20-mers that overlap by 10 amino acids and span the mature human PAP amino acid sequence. As shown in Figure 4, PAP1 and PAP2 antisera recognized 20-mer peptide containing the NFTLPSWA sequence, and PAP3 antiserum recognized the 20-mer peptides containing the QHEPYPL sequence. The analysis of PSA-specific antisera by ELISA using the overlapping peptides representing the PSA proteins did not identify any peptide that had a signal significantly higher than that for the background binding (not shown) thus confirming the lack of recognition of linear epitopes of the PSA in the analyzed PSAspecific antisera.Serum Antibody Repertoire ProfilingFigure 2. Motifs identified by MEME software for the 500 peptide lists for the PAP1, PAP, PAP3 and PAP4 antisera. doi:10.1371/journal.pone.0067181.gAnalyzing Antibody Repertoire of Human SerumThe described analysis of mouse sera using SAS demonstrates that the method can identify the antigen used for immunization, when the immune response involves recognition by serum antibodies of linear epitopes of the antigen. Next we wanted to evaluate the capability of the method to identify autoantigens recognized by serum antibodies produced in the absence of immunization. We analyzed a serum sample from the metastatic melanoma patient, assuming that the serum of a cancer patient can contain autoantibodies against proteins which are overexpressed or aberrantly expressed in tumor cells and had been exposed to the immune system due to tumor cell death. For the serum antibodies of the melanoma patient we identified the 500 most abundant peptides which were not shared with the list of peptides corresponding to the serum sample from a healthy donor. To identify the candidate autoantigens recognized by serum antibodies of the melanoma patients we used the same algorithm as we did for identifying the antigen used for immunization of mice. Table 3 shows the top 10 proteins ranked according to the final score calcul.Otein. For the PAP4 serum that did not produce significant matches to the PAP protein by BLAST analysis, all three motifs were represented equally. We also used MEME software to analyze the sequences of proteins that had been selected as the candidate antigens for the PAP1, PAP2 and the PAP3 sera based on their higher final score compared to the PAP isoforms. The MEME analysis identified the same motifs related to the NFTLPSWA and the QHEPYPL sequences of the PAP protein (Figure 3), suggesting that the PAP1, PAP2 and PAP3 sera could cross-react with these proteins. We also analyzed the PAP protein sequence using available online tool for linear epitope prediction http://sysbio.unl.edu/ SVMTriP/prediction.php. The software based on the Support Vector Machine algorithm predicted existence of three linear epitopes within the PAP sequence (Table 2). Although the NFTLPSWA sequence was not included in any of the predicted epitopes, the epitope predicted with the highest score included the QHEPYPL sequence recognized by the PAP3 antiserum. Anotherpredicted epitope contained the match to the peptide NTTNSHG from the PAP3 list, which retrieved the PAP isoforms by the BLAST searching of the peptide sequence against human refseq_protein database.Validating the SAS Results of Mouse Sera Profiling Using the Anti-peptide ELISATo prove 16985061 that the sequences identified by the SAS method represent the real linear epitopes recognized by serum antibodies, we analyzed PAP-specific antisera by ELISA using peptide library consisting of 20-mers that overlap by 10 amino acids and span the mature human PAP amino acid sequence. As shown in Figure 4, PAP1 and PAP2 antisera recognized 20-mer peptide containing the NFTLPSWA sequence, and PAP3 antiserum recognized the 20-mer peptides containing the QHEPYPL sequence. The analysis of PSA-specific antisera by ELISA using the overlapping peptides representing the PSA proteins did not identify any peptide that had a signal significantly higher than that for the background binding (not shown) thus confirming the lack of recognition of linear epitopes of the PSA in the analyzed PSAspecific antisera.Serum Antibody Repertoire ProfilingFigure 2. Motifs identified by MEME software for the 500 peptide lists for the PAP1, PAP, PAP3 and PAP4 antisera. doi:10.1371/journal.pone.0067181.gAnalyzing Antibody Repertoire of Human SerumThe described analysis of mouse sera using SAS demonstrates that the method can identify the antigen used for immunization, when the immune response involves recognition by serum antibodies of linear epitopes of the antigen. Next we wanted to evaluate the capability of the method to identify autoantigens recognized by serum antibodies produced in the absence of immunization. We analyzed a serum sample from the metastatic melanoma patient, assuming that the serum of a cancer patient can contain autoantibodies against proteins which are overexpressed or aberrantly expressed in tumor cells and had been exposed to the immune system due to tumor cell death. For the serum antibodies of the melanoma patient we identified the 500 most abundant peptides which were not shared with the list of peptides corresponding to the serum sample from a healthy donor. To identify the candidate autoantigens recognized by serum antibodies of the melanoma patients we used the same algorithm as we did for identifying the antigen used for immunization of mice. Table 3 shows the top 10 proteins ranked according to the final score calcul.
