Development of bioinformatic tools to identify and characterize linear protein epitopes
The adaptive immune system produces antibodies to specifically target antigens. Identifying and characterizing epitopes on target antigens enables numerous medical applications such as vaccine design, diagnostic discovery, and therapeutic antibody development. We have developed computational tools that characterize epitopes using large sets of antibody-binding peptides. To identify epitopes in target proteins, we used an approach termed K-mer Tiling of Protein Epitopes (K-TOPE). In this approach, we divided protein sequences into short overlapping subsequences of length k (k-mers). Then, we defined and scored epitopes using each k-mer’s enrichment in the sets of antibody-binding peptides. Using K-TOPE, we accurately identified epitopes for monoclonal and polyclonal antibodies. Next, using 250 specimens, we identified commonly targeted epitopes in nearly 3,000 viral proteins as well as two bacterial proteomes. Importantly, these epitopes agreed with previously reported results. To map antibody binding in epitopes, we developed Multiplexed Epitope Substitution Analysis (MESA). In this approach, target epitopes were divided into short overlapping k-mers. Then, each k-mer was exhaustively substituted with all amino acids. The effects of these substitutions were used to identify amino acid preferences at important binding positions in the epitopes. By applying this method to monoclonal antibodies and multiple sets of specimens, we identified binding motifs which agreed with an alternative computational approach. Finally, K-TOPE and MESA were used to characterize epitopes and antigens in age-related macular degeneration (AMD), herpes simplex virus (HSV), and Chagas disease. We identified 42 AMD-specific epitopes, 30 HSV2-specific epitopes, and 222 Chagas-specific epitopes. Several epitopes were in validated antigens while many were novel. MESA demonstrated that generally only 4-5 positions in these epitopes were important for binding. Future application of these approaches could enhance our understanding of the role that antibodies play in disease progression.