AI-Driven Structural Elucidation of the Bacteriophage KP32: Decoding Its Molecular Arsenal Against Klebsiella Pneumoniae

Background: Klebsiella pneumoniae is one of the most critical Gram-negative bacteria according to the World Health Organization (WHO). Due to the ability of this bacterium to evade antibiotics, phage therapy is becoming a promising tool. However, the use of isolated proteins rather than entire phages could reduce several risks associated with phage replication. Thus, understanding the protein composition and structural organization of bacteriophages is crucial for unlocking their biology and holds great potential for medicine and biotechnology. Methods: In this study, artificial intelligence with AlphaFold 3.0 (AF3) and bioinformatic analysis were used to model the hitherto unknown structure of the Klebsiella phage KP32 (KP32), a complex and selective phage that targets K. pneumoniae strains with the K3 and K21/KL163 capsular serotypes. Results: By combining AF3 with sequence and structure analysis, we reconstructed the entire phage KP32. This complex phage is composed of over 500 protein chains, of which 415 compose its capsid and 104 its core-portal-tail complex, a platform that allows the phage to adhere to K. pneumoniae, hydrolyze its capsular sugars and finally inject its genetic code into the bacterium. Conclusions: Phage therapy is a potentially promising tool for controlling antimicrobial resistance (AMR). However, one limitation arises from the limited knowledge of their nature and mechanisms of action, as only a few phages have been structurally characterized. The reconstruction of entire phages is currently a viable strategy for elucidating their mechanistic properties, knowledge that will enhance their potential applications as therapeutic alternatives.