Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.
Bioinformatics. 2020 Jul 1;36(13):3996-4003. doi: 10.1093/bioinformatics/btaa263.
Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the 'ground truth' of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens.
Our framework leverages three key aspects of antibody-antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction.
The source code is freely available on github at https://github.com/vamships/PECAN.git.
了解抗体如何特异性地与其抗原相互作用，可以帮助设计更好的药物和疫苗，并深入了解自然免疫。实验结构特征可以详细说明抗体-抗原相互作用的“真实情况”，但需要计算方法来有效地扩展到大规模研究。为了提高预测准确性，并为深入了解这些相互作用提供新的生物学见解，我们开发了一个基于深度学习的统一框架，以预测抗体和抗原上的结合界面。
我们的框架利用抗体-抗原相互作用的三个关键方面来学习预测结构表示：（i）由于界面是由空间接近的多个残基形成的，因此我们采用图卷积来聚合蛋白质中局部区域的属性；（ii）由于抗体-抗原对之间的相互作用是特异性的，因此我们采用注意力层来显式编码伙伴的上下文；（iii）由于更广泛的蛋白质-蛋白质相互作用的数据可用，因此我们采用迁移学习来利用这些数据作为抗体-抗原相互作用的先验。我们表明，这个单一的框架在预测抗体和抗原上的结合界面方面达到了最先进的性能，并且其三个方面中的每一个都可以提高性能。我们进一步表明，注意力层不仅可以提高性能，而且还可以提供对相互作用模式的生物学可解释视角。
源代码可在 github 上免费获取，网址为 https://github.com/vamships/PECAN.git。
