Abstract

Background: Predicting B-cell epitopes is important for understanding disease pathogenesis, identifying potential autoantigens, and designing vaccines and immune-based cancer therapies. The experimental approaches used for detecting B-cell epitopes are often laborious and resource-intensive. Thus, several computational methods have been developed for predicting the epitopes of a given antigen. However, most of these methods are coarse binary classifications of antigen regions within epitopes or non-epitopes and do not specify antibodies. Therefore, we aim to solve this antibodyspecified epitope prediction problem using a developed structure-based computational machine-learning method, Epitopia, to reflect this biological reality accurately.

Result: We selected 60 non-redundant antibody-antigen protein complexes to train and applied the leave-one-out cross-validation method to test the accuracy of our proposed methods; we compared the results with the Epitopia, Discotope 2.0,PEASE and the state-of-the-art tool SEPPA 2.0. We considered the role of both complementarity determining region residues and antigen surface residues in antigenantibody interactions and assigned a score for each antigen surface residue. If we considered a prediction to be successful if the average score of “epitope residues” exceeded the average score over all surface residues. Then the success rates of our proposed methods were all higher than Epitopia and the best one was 83.3%, which is 7% higher than the rate obtained with Epitopia (76%). The results show that antibody-specific methods are competitive with, and sometimes even better than, Epitopia, which only considers the antigen residues.

Conclusion: Our antibody-specific methods provide sufficient accuracy in locating epitopes because whether an antigen surface residue is an epitope depends on whether the antibody recognizes it. This approach is a new method for identifying B-cell epitopes. Based on our results, we believe that the prediction of antibody-specific B cell epitopes is more meaningful and efficient than those which are only considering antigen residues.

Keywords: Antibody-specific B-cell epitope, paratope-epitope residue pair, machine-learning, bioinformatics, antigen, epitopia.

Graphical Abstract