背景:蛋白质在各种生物过程中起着关键作用,精确预测蛋白质-蛋白质相互作用(PPI)位点对包括生物学在内的许多学科至关重要,医学和药学。虽然深度学习方法已经逐步被实施用于预测蛋白质中的PPI位点,提高其预测性能的任务仍然是一项艰巨的挑战。
结果:在本文中,我们提出了一种基于动态图卷积神经网络和两阶段迁移学习策略的PPI站点预测模型(DGCPPISP)。最初,我们从双重角度实施迁移学习,即特征输入和模型训练,为我们的模型提供有效的先验知识。随后,我们构建了一个为第二阶段培训设计的网络,建立在动态图卷积的基础上。
结论:为了评估其有效性,DGCPPISP模型的性能使用两个基准数据集进行审查。随后的结果表明,DGCPPISP在性能方面胜过竞争方法。具体来说,DGCPPISP超越了第二好的方法,EGRET,按5.9%的利润率计算,10.1%,F1测量为13.3%,AUPRC,和MCC度量分别在Dset_186_72_PDB164上。同样,在Dset_331上,它使亚军方法的性能黯然失色,HN-PPISP,14.5%,19.8%,和分别为29.9%。
BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep learning methods have progressively been implemented for the prediction of PPI sites within proteins, the task of enhancing their predictive performance remains an arduous challenge.
RESULTS: In this paper, we propose a novel PPI site prediction model (DGCPPISP) based on a dynamic graph convolutional neural network and a two-stage transfer learning strategy. Initially, we implement the transfer learning from dual perspectives, namely feature input and model training that serve to supply efficacious prior knowledge for our model. Subsequently, we construct a network designed for the second stage of training, which is built on the foundation of dynamic graph convolution.
CONCLUSIONS: To evaluate its effectiveness, the performance of the DGCPPISP model is scrutinized using two benchmark datasets. The ensuing results demonstrate that DGCPPISP outshines competing methods in terms of performance. Specifically, DGCPPISP surpasses the second-best method, EGRET, by margins of 5.9%, 10.1%, and 13.3% for F1-measure, AUPRC, and MCC metrics respectively on Dset_186_72_PDB164. Similarly, on Dset_331, it eclipses the performance of the runner-up method, HN-PPISP, by 14.5%, 19.8%, and 29.9% respectively.