IEEE/ACM Trans Comput Biol Bioinform. 2023 May-Jun;20(3):2089-2100. doi: 10.1109/TCBB.2022.3233627. Epub 2023 Jun 5.
Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://github.com/lennylv/DGCddG.
有效准确地预测氨基酸突变后蛋白质相互作用的效果，是理解蛋白质功能机制和药物设计的关键问题。在这项研究中，我们提出了一个基于深度图卷积（DGC）网络的框架 DGCddG，用于预测突变后蛋白质-蛋白质结合亲和力的变化。DGCddG 结合了多层图卷积，为蛋白质复合物结构的每个残基提取深度、上下文化的表示。然后，通过 DGC 挖掘的突变位点通道与多层感知机拟合结合亲和力。在多个数据集上的实验结果表明，我们的模型对于单点和多点突变都能取得较好的性能。对于与 SARS-CoV-2 病毒结合的血管紧张素转换酶 2 的相关数据集的盲测，我们的方法在预测 ACE2 变化方面表现出更好的结果，可能有助于寻找有利的抗体。代码和数据可在 https://github.com/lennylv/DGCddG 上获取。
