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.

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.

Understanding antibody function is often enhanced by knowledge of the specific binding epitope. Here, we describe a computer algorithm that permits epitope prediction based on a collection of random peptide epitopes (mimotopes) isolated by antibody affinity purification. We applied this methodology to the prediction of epitopes for five monoclonal antibodies against the West Nile virus (WNV) E protein, two of which exhibit therapeutic activity in vivo. This strategy was validated by comparison of our results with existing F(ab)-E protein crystal structures and mutational analysis by yeast surface display. We demonstrate that by combining the results of the mimotope method with our data from mutational analysis, epitopes could be predicted with greater certainty. The two methods displayed great complementarity as the mutational analysis facilitated epitope prediction when the results with the mimotope method were equivocal and the mimotope method revealed a broader number of residues within the epitope than the mutational analysis. Our results demonstrate that the combination of these two prediction strategies provides a robust platform for epitope characterization.

Applying bioinformatics for antibody epitope prediction using affinity-selected mimotopes - relevance for vaccine design.

To properly characterize protective polyclonal antibody responses, it is necessary to examine epitope specificity. Most antibody epitopes are conformational in nature and, thus, cannot be identified using synthetic linear peptides. Cyclic peptides can function as mimetics of conformational epitopes (termed mimotopes), thereby providing targets, which can be selected by immunoaffinity purification. However, the management of large collections of random cyclic peptides is cumbersome. Filamentous bacteriophage provides a useful scaffold for the expression of random peptides (termed phage display) facilitating both the production and manipulation of complex peptide libraries. Immunoaffinity selection of phage displaying random cyclic peptides is an effective strategy for isolating mimotopes with specificity for a given antiserum. Further epitope prediction based on mimotope sequence is not trivial since mimotopes generally display only small homologies with the target protein. Large numbers of unique mimotopes are required to provide sufficient sequence coverage to elucidate the target epitope. We have developed a method based on pattern recognition theory to deal with the complexity of large collections of conformational mimotopes. The analysis consists of two phases: 1) The learning phase where a large collection of epitope-specific mimotopes is analyzed to identify epitope specific "signs" and 2) The identification phase where immunoaffinity-selected mimotopes are interrogated for the presence of the epitope specific "signs" and assigned to specific epitopes. We are currently using computational methods to define epitope "signs" without the need for prior knowledge of specific mimotopes. This technology provides an important tool for characterizing the breadth of antibody specificities within polyclonal antisera.