Identification of Unique Peptide Motifs as Linear epitopes with Pfam Domains and Families

Abstract

To identify the unique peptide motifs (UPMs) as specific linear epitopes from all family protein sequences, we have developed a database called linear epitope prediction database (LEPD) which applies reinforced merging techniques, background model analysis, and chemical property analysis to interpret its specific antigenicity. The UPMs extracted from more than 8,000 families defined by Pfam are regarded as linear epitopes providing significant information for the designs of antibodies and vaccines. They can be verified as epitope candidates according to their biological properties and compositions of the continuous stretch of amino acid residues. These potential epitopes predicted from each protein family can be revealed in a straightforward manner from the proposed database, and the corresponding chemical properties of each predicted epitopes are illustrated in graphical and tabular forms. Each identified UPM can be analyzed by scanning through the complete genomes of various organisms for guaranteeing the specificity of antigenic peptides. For any query protein possessing resolved three-dimensional structure, the contents in the proposed database also provide interactive visualization of protein structures for the allocation and comparison of the predicted linear epitopes. In terms of mapping a UPM as a linear epitope, the accuracy of the algorithm for linear epitope prediction is evaluated to be higher than 70% in comparison with known databases.