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利用人工智能优化噬菌体疗法:一种观点。

Optimizing phage therapy with artificial intelligence: a perspective.

作者信息

Doud Michael B, Robertson Jacob M, Strathdee Steffanie A

机构信息

Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California, San Diego, San Diego, CA, United States.

Department of Ecology, Behavior & Evolution, School of Biological Sciences, University of California, San Diego, La Jolla, CA, United States.

出版信息

Front Cell Infect Microbiol. 2025 May 27;15:1611857. doi: 10.3389/fcimb.2025.1611857. eCollection 2025.

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.

摘要

噬菌体疗法正成为应对日益增长的抗菌耐药性威胁的一种有前景的策略,然而噬菌体和细菌在彼此相互作用方面极其多样且具有独特性。噬菌体疗法的临床应用通常依赖于一个手动筛选天然存在的噬菌体库以检测其对特定临床分离细菌活性的过程,这是一项劳动密集型任务,而且不一定能产生对特定分离株具有最佳活性的噬菌体。在此,我们综述了人工智能(AI)方法的最新进展,这些进展正在推动噬菌体 - 宿主相互作用的研究,有望实现更有效的噬菌体疗法设计。鉴于合成生物学的同步进展使得能够对噬菌体进行快速基因操作,我们设想这些源自人工智能的见解如何为下一代合成噬菌体的基因优化提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ef8/12149144/64ea892d007b/fcimb-15-1611857-g001.jpg

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