Department of Physics, Sapienza University, 00184 Rome, Italy.
Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht University, Utrecht 3584CH, The Netherlands.
Bioinformatics. 2020 Dec 22;36(20):5107-5108. doi: 10.1093/bioinformatics/btaa644.
Monoclonal antibodies are essential tools in the contemporary therapeutic armory. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalyzing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody-antigen complexes.
Here, we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK.
The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/.
Supplementary data are available at Bioinformatics online.
单克隆抗体是当代治疗武器库中的重要工具。了解这些抗体如何识别它们的抗原是对其进行合理设计和工程改造的基本步骤。越来越多的公开可用数据正在推动计算方法的发展，这些方法能够为研究抗体-抗原复合物的经典实验方法提供有价值、更快和更便宜的替代方案。
在这里，我们提出了 proABC-2，这是原始随机森林抗体变构预测器的更新版本，基于卷积神经网络算法。我们还展示了如何将预测结果有效地用于引导 HADDOCK 对接。
proABC-2 服务器可免费在以下网址获得：https://wenmr.science.uu.nl/proabc2/。
补充数据可在“Bioinformatics”在线获得。
