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

被引：13

作者：

Chinery, Lewis ^{[1
]}

Wahome, Newton ^{[2
]}

Moal, Iain ^{[3
]}

Deane, Charlotte M. ^{[1
]}

机构：

[1] Univ Oxford, Dept Stat, Oxford OX1 3LB, England

[2] GSK Vaccines, Rockville, MD 20850 USA

[3] GlaxoSmithKline Res & Dev Ltd, Stevenage SG1 2NY, England

来源：

BIOINFORMATICS | 2023年 / 39卷 / 01期

基金：

英国生物技术与生命科学研究理事会;

关键词：

D O I：

10.1093/bioinformatics/btac732

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页数：3

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