Paragraph—antibody paratope prediction using graph neural networks with minimal feature vectors
Lewis Chinery, Newton Wahome, Iain H. Moal, Charlotte M. Deane
Abstract
SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at www.github.com/oxpig/Paragraph. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Topics & Concepts
ParatopeParagraphComputer scienceFeature (linguistics)GraphArtificial intelligenceAntibodyTheoretical computer scienceEpitopeImmunologyWorld Wide WebBiologyLinguisticsPhilosophyMonoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approachesImmunotherapy and Immune Responses