小柯机器人

美国斯坦福大学Peter S. Kim和Brian L. Hie共同合作，近期取得重要工作进展。他们研究提出了基于一般蛋白质语言模型的人类抗体的高效演化方法。相关研究成果2023年4月24日在线发表于《自然—生物技术》杂志上。

据介绍，自然演化必须探索大量可能的序列，以获得理想但罕见的突变，这表明从自然演化策略中学习可以指导人工演化。

研究人员报告了一般的蛋白质语言模型可以通过提出演化上合理的突变来有效地演化人类抗体，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。研究人员对七种抗体进行了语言模型引导的亲和力成熟，仅在两轮实验室演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度成熟抗体的结合亲和力提高到7倍，将三种未成熟抗体的结合亲和力提高到160倍，许多设计也证明了对埃博拉和严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）假病毒具有良好的热稳定性和病毒中和活性。

总之，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（包括抗生素耐药性和酶活性）的有效演化，表明这些结果适用于许多环境。

附：英文原文

Title: Efficient evolution of human antibodies from general protein language models

Author: Hie, Brian L., Shanker, Varun R., Xu, Duo, Bruun, Theodora U. J., Weidenbacher, Payton A., Tang, Shaogeng, Wu, Wesley, Pak, John E., Kim, Peter S.

Issue&Volume: 2023-04-24

Abstract: 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.

DOI: 10.1038/s41587-023-01763-2

Source: https://www.nature.com/articles/s41587-023-01763-2

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex