Yang Xiaodi, Yang Shiping, Ren Panyu, Wuchty Stefan, Zhang Ziding
State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, China.
Front Microbiol. 2022 Apr 15;13:842976. doi: 10.3389/fmicb.2022.842976. eCollection 2022.
Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.
识别人类病毒蛋白质-蛋白质相互作用（PPI）是理解病毒感染机制和人类宿主抗病毒反应的关键步骤。高通量实验技术的最新进展使得人类病毒PPI数据大量积累，这进一步推动了基于机器学习的人类病毒PPI预测方法的发展。深度学习作为一种非常有前景的预测人类病毒PPI的方法，展现出强大的能力，能够整合大规模数据集，学习蛋白质复杂的序列-结构关系，并将学到的模式转化为高精度的最终预测模型。聚焦于深度学习助力的人类病毒PPI预测的最新进展，我们回顾了这些新开发方法的技术细节，包括数据集准备、深度学习架构、特征工程和性能评估。此外，我们讨论了当前面临的挑战和潜在解决方案，并展望了在后AlphaFold2时代人类病毒PPI预测的未来前景。
