Xing Zhao , Ming Li , Congbiao You

Tropical Microbial Resources Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572025, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 5   
Received: 03 Jul., 2025    Accepted: 11 Aug., 2025    Published: 05 Sep., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The protein-protein Interactions (PPI) network of pathogenic bacteria plays a significant role in the pathogenic mechanism of bacteria and the development of drug resistance, and it is a key entry point for systems biology and new drug research and development. However, traditional PPI prediction methods (such as yeast two-hybrid and co-immunoprecipitation, etc.) have limitations such as high cost, long cycle, limited coverage, and the results are easily disturbed by noise. In recent years, the rise of machine learning, especially deep learning, has brought revolutionary progress to PPI research. With its powerful nonlinear modeling and automatic feature extraction capabilities, it has broken through the bottleneck of manual feature engineering. This paper reviews the application progress of machine learning techniques in predicting protein-protein interactions of pathogenic bacteria, with a focus on how supervised, unsupervised and deep learning methods overcome the limitations of traditional methods and improve prediction performance. Meanwhile, we discuss the impact of data preprocessing and feature engineering strategies on the model, summarize the construction and evaluation methods of machine learning models, as well as the application achievements of these models in revealing antibiotic resistance mechanisms, vaccine target screening, cross-species interactions, and other aspects. Through a case study of deep learning prediction in a Salmonella protein-protein interaction network, we verified the effectiveness and biological significance of deep learning models, and looked forward to the current challenges and future development directions.

Pathogenic bacteria; Protein-protein interactions; Machine learning; Deep learning; Graph neural network