Efficient evolution of human antibodies from general protein language models
Efficient evolution of human antibodies from general protein language models
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DOI：
10.1038/s41587-023-01763-2
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发表时间：
2023-04-24
影响因子：
46.9
通讯作者：
Kim, Peter S. S.
中科院分区：
文献类型：
--
作者：
Hie, Brian L. L.;Shanker, Varun R. R.;Kim, Peter S. S.
自然进化必须探索广阔的可能序列图景，以寻找理想但罕见的突变，这表明从自然进化策略中学习可以指导人工进化。在这里，我们报告了一般的蛋白质语言模型可以通过建议在进化上可信的突变来有效地进化人类抗体，尽管该模型没有提供关于靶抗原、结合特异性或蛋白质结构的信息。我们对七种抗体进行了语言模型引导的亲和力成熟，仅在两轮实验室进化中筛选出每种抗体的20种或更少的变体，并将四种临床相关的高成熟抗体的结合亲和力提高了七倍，将三种未成熟抗体的结合亲和力提高了160倍，许多设计还显示了良好的热稳定性和对埃博拉和严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)伪病毒的病毒中和活性。改善抗体结合的相同模型也指导了不同蛋白质家族和选择压力的有效进化，包括抗生素耐药性和酶活性，这表明这些结果适用于许多环境。通用的蛋白质语言模型指导蛋白质进化，需要20个或更少的变异进行测试。
Natural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could guide artificial evolution. Here we report that general protein language models can efficiently evolve human antibodies by suggesting mutations that are evolutionarily plausible, despite providing the model with no information about the target antigen, binding specificity or protein structure. We performed language-model-guided affinity maturation of seven antibodies, screening 20 or fewer variants of each antibody across only two rounds of laboratory evolution, and improved the binding affinities of four clinically relevant, highly mature antibodies up to sevenfold and three unmatured antibodies up to 160-fold, with many designs also demonstrating favorable thermostability and viral neutralization activity against Ebola and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudoviruses. The same models that improve antibody binding also guide efficient evolution across diverse protein families and selection pressures, including antibiotic resistance and enzyme activity, suggesting that these results generalize to many settings.A general protein language model guides protein evolution with 20 or fewer variants needed for testing.