Optimizing phage therapy with artificial intelligence: a perspective

Abstract

Phage therapy is emerging as a promising strategy against the growing threat of antimicrobial resistance, yet phage and bacteria are incredibly diverse and idiosyncratic in their interactions with one another. Clinical applications of phage therapy often rely on a process of manually screening collections of naturally occurring phages for activity against a specific clinical isolate of bacteria, a labor-intensive task that is not guaranteed to yield a phage with optimal activity against a particular isolate. Herein, we review recent advances in artificial intelligence (AI) approaches that are advancing the study of phage-host interactions in ways that might enable the design of more effective phage therapeutics. In light of concurrent advances in synthetic biology enabling rapid genetic manipulation of phages, we envision how these AI-derived insights could inform the genetic optimization of the next generation of synthetic phages.

Keywords: artificial intelligence; gene discovery; machine learning; phage engineering; phage specificity; phage therapy; synthetic biology.

Figure 1

Overview of recent advances and future vision for AI methods to optimize phage therapy. Top: Phage matching based on genetic features in phage and bacterial genome sequences using AI-based algorithms can help identify candidate phages within phage banks for a provided patient isolate bacterium. Bottom: AI algorithms can predict functional phage genes from large sequence databases. Desired phage functions can be genetically grafted onto synthetic phages and evaluated for enhanced phage activity. Created in BioRender.