Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors

Chinery, L.; Wahome, N.; Moal, I. H.; Deane, C. M.

doi:10.1101/2022.06.10.495640

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bioRxiv · 10.1101/2022.06.10.495640

Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors

Chinery, L.·Wahome, N.·Moal, I. H.·Deane, C. M.

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Abstract

1SummaryThe 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. AvailabilitySource code is freely available at www.github.com/oxpig Contactdeane@stats.ox.ac.uk Supplementary informationSupplementary data are available at bioRxiv online.

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Chinery, L., Wahome, N., Moal, I. H., Deane, C. M.. 2022-06-13. Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors. https://doi.org/10.1101/2022.06.10.495640

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