Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However, this is difficult even for short peptides owing to the large combinatorial space of amino acid sequences. Here we describe a method for measuring the loss or maintenance of antimicrobial-peptide activity for thousands of peptide-sequence variants simultaneously, and its application to Protegrin-1, a potent yet toxic antimicrobial peptide, to determine the positional importance and flexibility of residues across its sequence while identifying variants with changes in membrane selectivity. More bacterially selective variants maintained a membrane-bound secondary structure while avoiding aromatic residues and cysteine pairs. A machine-learning model trained with our datasets accurately predicted membrane-specific activities for over 5.7 million Protegrin-1 variants, and identified one variant that showed substantially reduced toxicity and retention of activity in a mouse model of intraperitoneal infection. The high-throughput methodology may help elucidate sequence–structure–function relationships in antimicrobial peptides and inform the design of peptide-based synthetic drugs.

许多抗菌肽直接破坏细菌膜，但也会损害哺乳动物膜。因此，破译抗菌肽膜选择性的规则对其治疗用途至关重要。然而，由于氨基酸序列的组合空间很大，即使对于短肽来说这也是困难的。在这里，我们描述了一种同时测量数千种肽序列变体抗菌肽活性丧失或维持的方法，及其在 Protegrin-1（一种有效但有毒的抗菌肽）中的应用，以确定残基的位置重要性和灵活性其序列，同时识别膜选择性变化的变体。更多的细菌选择性变体保持了膜结合的二级结构，同时避免了芳香族残基和半胱氨酸对。使用我们的数据集训练的机器学习模型准确预测了超过 570 万个 Protegrin-1 变体的膜特异性活性，并确定了一种在腹膜内感染小鼠模型中显示出毒性显着降低和活性保留的变体。高通量方法可能有助于阐明抗菌肽的序列-结构-功能关系，并为基于肽的合成药物的设计提供信息。