Artificial intelligence approaches to human-microbiome protein-protein interactions.

Area of Science:

• Microbiology
• Computational Biology
• Bioinformatics

Background:

• Host-microbiome interactions are crucial for human health and disease.
• Understanding molecular interplay requires advanced computational tools.

Purpose of the Study:

• To review computational methods for predicting microbial effects on human cells.
• To focus on protein-protein interactions in host-microbiome research.
• To guide future directions in the field.

Main Methods:

• Categorization of computational methods from traditional to deep learning approaches.
• Analysis of structure-based approaches for predicting interactions.
• Review of challenges and potential solutions in computational prediction.

Main Results:

• Overview of diverse computational strategies for host-microbiome analysis.
• Identification of deep learning as a key advancement.
• Discussion of limitations and future research avenues in structure-based prediction.

Conclusions:

• Computational methods, especially AI and deep learning, are vital for dissecting host-microbiome molecular interactions.
• Structure-based approaches present challenges but offer future potential.
• This review provides a roadmap for advancing the field.