Motivation Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in vivo and in vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody–antigen binding for antigens with no known antibody binders. Results We demonstrate that DLAB can be used both to improve antibody–antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody–antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies. Availability and implementation The DLAB source code and pre-trained models are available at https://github.com/oxpig/dlab-public. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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DLAB：基于结构的抗体虚拟筛选的深度学习方法

Motivation 抗体是最重要的药物类别之一，目前有超过 80 种经批准的分子用于治疗多种疾病。然而，抗体治疗候选药物的发现过程是时间和成本密集型的​​，并且严重依赖于体内和体外高通量筛选。在这里，我们介绍了一个基于结构的抗体深度学习框架 (DLAB)，它可以虚拟地筛选针对目标抗原靶点的假定结合抗体。构建 DLAB 是为了能够在没有已知抗体结合剂的情况下预测抗原的抗体-抗原结合。结果我们证明 DLAB 可用于改善抗体-抗原对接和基于结构的候选抗体虚拟筛选。DLAB 可以改进抗体对接实验的姿势排序，以及选择生成准确姿势并正确排序的抗体-抗原配对。我们还在案例研究中表明 DLAB 可以识别针对特定抗原的结合抗体。我们的结果证明了深度学习方法在基于结构的抗体虚拟筛选中的前景。可用性和实施​​ DLAB 源代码和预训练模型可在 https://github.com/oxpig/dlab-public 获得。补充信息 补充数据可在 Bioinformatics 在线获取。我们的结果证明了深度学习方法在基于结构的抗体虚拟筛选中的前景。可用性和实施​​ DLAB 源代码和预训练模型可在 https://github.com/oxpig/dlab-public 获得。补充信息 补充数据可在 Bioinformatics 在线获取。我们的结果证明了深度学习方法在基于结构的抗体虚拟筛选中的前景。可用性和实施​​ DLAB 源代码和预训练模型可在 https://github.com/oxpig/dlab-public 获得。补充信息 补充数据可在 Bioinformatics 在线获取。