The component parts of bacteriophage virions accurately defined by a new machine-learning approach built on evolutionary features.

Klebsiella pneumoniae has risen to prominence as a major threat to human health, with hypervirulent and drug-resistant lineages spreading globally. Given their antimicrobial resistant phenotypes, new therapies are required for the treatment of these infections, and bacteriophages (phages) that kill Klebsiella are being identified for use in phage therapy. In order to circumvent the evolution of phage-resistance taking hold the way that drug-resistance has, clear and considered actions are needed in selecting the phages that would be used in therapeutic cocktails. It is known that annotation of phage genomes is poor, potentially obscuring those phages with the most therapeutic potential. Here we show that phages isolated from infrequently sampled environments have features of therapeutic potential and developed a computational tool called STEP3 to understand the evolutionary features that distinguish the component parts of diverse phages, features that proved particularly suitable to detection of virion proteins with only distantly related homologies. These features were integrated into an ensemble framework to achieve a stable and robust prediction performance by STEP3. Proteomics-based analysis of two phages validated the prediction accuracy of STEP3 and revealed the virions contain component parts that include DNA-binding factors, otherwise unrecognizable capsule degradation enzymes and membrane translocation factors.

肺炎克雷伯菌（Klebsiella pneumoniae）已成为威胁人类健康的重大威胁，其高毒力与耐药谱系在全球范围内广泛传播。鉴于其抗菌耐药表型，亟需开发新型疗法以治疗此类感染，目前学界正针对可杀灭克雷伯菌的噬菌体（bacteriophages，简称phages）开展筛选工作，用于噬菌体疗法。为避免噬菌体抗性像药物抗性一样演化扩散，在筛选用于治疗性噬菌体鸡尾酒的组成成分时，需采取审慎周全的明确举措。已知噬菌体基因组注释质量欠佳，可能会掩盖那些最具治疗潜力的噬菌体。本研究表明，从罕见采样环境中分离的噬菌体具备治疗潜力相关特征；此外，我们开发了一款名为STEP3的计算工具，用于解析区分不同噬菌体组成成分的演化特征，这类特征尤其适用于仅存在极远缘同源性的病毒衣壳蛋白的识别。STEP3将这些特征整合至集成框架中，实现了稳定且可靠的预测性能。通过对两株噬菌体开展基于蛋白质组学的分析，验证了STEP3的预测准确性，并揭示其病毒衣壳包含DNA结合因子、此前难以识别的荚膜降解酶以及膜转运因子等组成成分。