School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Bioinformatics. 2024 Jun 28;40(Suppl 1):i418-i427. doi: 10.1093/bioinformatics/btae232.
Mutations are the crucial driving force for biological evolution as they can disrupt protein stability and protein-protein interactions which have notable impacts on protein structure, function, and expression. However, existing computational methods for protein mutation effects prediction are generally limited to single point mutations with global dependencies, and do not systematically take into account the local and global synergistic epistasis inherent in multiple point mutations.
To this end, we propose a novel spatial and sequential message passing neural network, named DDAffinity, to predict the changes in binding affinity caused by multiple point mutations based on protein 3D structures. Specifically, instead of being on the whole protein, we perform message passing on the k-nearest neighbor residue graphs to extract pocket features of the protein 3D structures. Furthermore, to learn global topological features, a two-step additive Gaussian noising strategy during training is applied to blur out local details of protein geometry. We evaluate DDAffinity on benchmark datasets and external validation datasets. Overall, the predictive performance of DDAffinity is significantly improved compared with state-of-the-art baselines on multiple point mutations, including end-to-end and pre-training based methods. The ablation studies indicate the reasonable design of all components of DDAffinity. In addition, applications in nonredundant blind testing, predicting mutation effects of SARS-CoV-2 RBD variants, and optimizing human antibody against SARS-CoV-2 illustrate the effectiveness of DDAffinity.
DDAffinity is available at https://github.com/ak422/DDAffinity.
突变是生物进化的关键驱动力，因为它们可以破坏蛋白质稳定性和蛋白质-蛋白质相互作用，这对蛋白质结构、功能和表达有显著影响。然而，现有的蛋白质突变效应预测计算方法通常仅限于具有全局依赖性的单点突变，并且不能系统地考虑多点突变中固有的局部和全局协同上位效应。
为此，我们提出了一种新的基于空间和顺序消息传递的神经网络，称为 DDAffinity，用于基于蛋白质 3D 结构预测多点突变引起的结合亲和力变化。具体来说，我们不是在整个蛋白质上，而是在 k-最近邻残基图上执行消息传递，以提取蛋白质 3D 结构的口袋特征。此外，为了学习全局拓扑特征，在训练过程中应用了两步加性高斯噪声策略，以模糊蛋白质几何形状的局部细节。我们在基准数据集和外部验证数据集上评估了 DDAffinity。总体而言，与基于端到端和预训练的最新基线方法相比，DDAffinity 在多点突变方面的预测性能有了显著提高。消融研究表明 DDAffinity 的所有组件的设计都是合理的。此外，在非冗余盲测中的应用、预测 SARS-CoV-2 RBD 变体的突变效应以及优化针对 SARS-CoV-2 的人源抗体表明了 DDAffinity 的有效性。
DDAffinity 可在 https://github.com/ak422/DDAffinity 上获得。
