Advances in the Application of Artificial Intelligence to Predict Host-Pathogen Protein Interactions
Host-pathogen protein-protein interaction(HP-PPI)is a key molecular event in host during infection by pathogens.Elucidation of HP-PPI is crucial for understanding the immune defense mechanism of the host,the mechanism of pathogenesis,and development of anti-infection drugs.In recent years,the development of PPI detection methods and their application in host-pathogen interaction studies have accumulated a large amount of HP-PPI data,which help the artificial intelligence(AI)emerge as outstanding techniques in the research field of HP-PPI prediction.This paper reviews the application of AI techniques in HP-PPI prediction.Firstly,the workflow of AI-aided identification of HP-PPI is outlined,and the commonly used databases containing HP-PPI data are summarized.Subsequently,we focus on the application of two major categories of AI methods,namely machine learning and deep learning,in the research field of HP-PPI prediction,and present the fundamentals of several classical algorithmic models,feature selection methods and model evaluation results.Finally,the problems and challenges faced by AI-aided HP-PPI prediction were discussed in detail to provide insights for researchers in studying host-pathogen interactions.
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