A machine-learning approach for predicting B-cell epitopes.

The immune activity of an antibody is directed against a specific region on its target antigen known as the epitope. Numerous immunodetection and immunotheraputics applications are based on the ability of antibodies to recognize epitopes. The detection of immunogenic regions is often an essential step in these applications. The experimental approaches used for detecting immunogenic regions are often laborious and resource-intensive. Thus, computational methods for the prediction of immunogenic regions alleviate this drawback by guiding the experimental procedures. In this work we developed a computational method for the prediction of immunogenic regions from either the protein three-dimensional structure or sequence when the structure is unavailable. The method implements a machine-learning algorithm that was trained to recognize immunogenic patterns based on a large benchmark dataset of validated epitopes derived from antigen structures and sequences. We compare our method to other available tools that perform the same task and show that it outperforms them.