Deciphering epitope specificities within polyserum using affinity selection of random peptides and a novel algorithm based on pattern recognition theory

While numerous strategies have been developed to map epitope specificities for monoclonal antibodies, few have been designed for elucidating epitope specificity within complex polysera. We have developed a novel algorithm based on pattern recognition theory that can be used to characterize the breadth of epitope specificities within a polyserum based on affinity selection of random peptides. To attribute these random peptides to a specific epitope, the sequences of the affinity-selected peptides were matched against a database of random peptides selected using well-described monoclonal antibodies. To test this novel algorithm, we employed polyserum from patients infected with West Nile virus and isolated 109 unique sequences which were recognized selectively by serum from West Nile virus-infected patients but not uninfected patients. Through application of our algorithm, it was possible to match 20% of the polyserum-selected peptides to the database of peptides isolated by affinity selection using monoclonal antibodies against the virus envelope protein. Statistical analysis demonstrated that the peptides selected with the polyserum could not be attributed to the peptide database by chance. This novel algorithm provides the basis for further development of methods to characterize the breadth of epitope recognition within a complex pool of antibodies.