Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein-protein interactions as potential targets for antibacterial drug development. Available at: https://github.com/SysBioUAB/hpi_predictor.

大多数细胞过程都涉及蛋白质-蛋白质相互作用 (PPI)。不幸的是，目前对宿主-病原体相互作用组的了解仍然非常有限。用于检测 PPI 的实验方法有几个局限性，包括在大规模筛查中增加复杂性和经济成本。因此，计算方法通常用于支持实验数据，尽管它们通常存在高误报率。为了解决这个问题，我们创建了 HPIPred，这是一种基于物理化学特性的数字编码的宿主病原体 PPI 预测工具。与其他可用方法不同，HPIPred 整合了表型数据以优先考虑具有生物学意义的结果。我们使用 HPIPred 筛选了整个智人和铜绿假单胞菌 PAO1 蛋白质组，以生成具有 763 种相互作用的宿主-病原体相互作用组，显示出高度连接的网络拓扑结构。我们的预测模型可用于优先考虑蛋白质-蛋白质相互作用作为抗菌药物开发的潜在目标。可在：https://github.com/SysBioUAB/hpi_predictor。