Paragraph-antibody paratope prediction using graph neural networks with minimal feature vectors.

Abstract

The development of new vaccines and antibody therapeutics typically takes several years and requires over $1bn in investment. Accurate knowledge of the paratope (antibody binding site) can speed up and reduce the cost of this process by improving our understanding of antibody-antigen binding. We present Paragraph, a structure-based paratope prediction tool that outperforms current state-of-the-art tools using simpler feature vectors and no antigen information. Source code is freely available at www.github.com/oxpig/Paragraph. Supplementary data are available at Bioinformatics online.