Prediction of antibody response using recombinant human protein fragments as antigen.
A great need exists for prediction of antibody response for the generation of antibodies toward protein targets. Earlier studies have suggested that prediction methods based on hydrophilicity propensity scale, in which the degree of exposure of the amino acid in an aqueous solvent is calculated, has limited value. Here, we show a comparative analysis based on 12,634 affinity-purified antibodies generated in a standardized manner against human recombinant protein fragments. The antibody response (yield) was measured and compared to theoretical predictions based on a large number (544) of published propensity scales. The results show that some of the scales have predictive power, although the overall Pearson correlation coefficient is relatively low (0.2) even for the best performing amino acid indices. Based on the current data set, a new propensity scale was calculated with a Pearson correlation coefficient of 0.25. The values correlated in some extent to earlier scales, including large penalty for hydrophobic and cysteine residues and high positive contribution from acidic residues, but with relatively low positive contribution from basic residues. The fraction of immunogens generating low antibody responses was reduced from 30% to around 10% if immunogens with a high propensity score (>0.48) were selected as compared to immunogens with lower scores (<0.29). The study demonstrates that a propensity scale might be useful for prediction of antibody response generated by immunization of recombinant protein fragments. The data set presented here can be used for further studies to design new prediction tools for the generation of antibodies to specific protein targets.
Rockberg
Johan
J
School of Biotechnology, Royal Institute of Technology, AlbaNova University Center, Stockholm SE-106 91, Sweden.
Uhlén
Mathias
M
D000465:Algorithms; D000917:Antibody Formation / Q000276:immunology*; D000941:Antigens / Q000235:genetics / Q000276:immunology; D019295:Computational Biology / Q000379:methods; D005796:Genes / Q000276:immunology; D006801:Humans; D007114:Immunization; D016014:Linear Models; D018448:Models, Immunological; D010446:Peptide Fragments / Q000276:immunology*; D025941:Protein Interaction Mapping; D011994:Recombinant Proteins / Q000235:genetics / Q000276:immunology*
D000941:Antigens; D010446:Peptide Fragments; D011994:Recombinant Proteins