Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.

分子灭绝旨在复活分子以解决抗生素耐药性和其他当今的生物学和生物医学问题。在这里，我们展示了深度学习可用于挖掘所有现有灭绝生物的蛋白质组，以发现抗生素肽。我们训练了由肽序列编码器和神经网络组成的深度学习模型集合，用于预测抗菌活性，并用它来挖掘 10,311,899 个肽。该模型预测了 37,176 个具有广谱抗菌活性的序列，其中 11,035 个在现有生物体中未发现。我们合成了 69 种肽，并通过实验证实了它们对细菌病原体的活性。大多数肽通过使细菌的细胞质膜去极化来杀死细菌，这与已知的抗菌肽相反，后者倾向于靶向外膜。值得注意的是，铅化合物（包括来自猛犸象的 mammuthusin-2、来自直牙象的 elephasin-2、来自古代海牛的 Hydrodamin-1、来自巨型树懒的 mylodonin-2 和来自已灭绝巨型麋鹿的 megalocerin-1）在患有皮肤脓肿或大腿感染的小鼠中显示出抗感染活性。深度学习辅助的分子去灭绝可能会加速治疗分子的发现。