ArticleFrontiers in cellular and infection microbiology2025
Uncovering the potential virulence factors of emerging pathogens using AI/ML-based tools: a case study in
Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: We are currently in the era of artificial intelligence (AI), which has become deeply embedded across nearly all scientific disciplines. Harnessing this revolutionary technology to predict virulence factors of emerging pathogens can improve our understanding of their pathogenicity, especially since the majority of these pathogens' proteomes are composed of hypothetical or uncharacterized proteins. Moreover, emerging orphan proteins were expressed from novel open reading frames. Therefore, this study aimed to develop a pipeline for predicting and annotating the species-specific secreted protein structures of these pathogens, with Methods: The proteome of Results: The structure modeling and homologous matching revealed several protein domains similar to toxins (scorpion toxin-like, cytolysin, CARDS toxin, defensin-like), allergens, adhesins, hydrolytic enzymes, and inhibitors. Novel domains with putative functions (ion binding, proteolysis, transferase activity, and protein binding) were also discovered. In immunoinformatics and molecular docking studies, a cytolysin like-containing protein (Gene ID: ACJ72_08076) outperformed the other selected proteins in binding to MHC-II (Docking score = -318.74) with a confidence score = 0.96. Conclusion: The findings suggest that AI and ML tools can be employed in the preliminary stage to explore host-pathogen interactions and anticipate novel virulence genes.
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