蛋白质工程
合成生物学
计算生物学
病毒学
抗体
噬菌体展示
肽库
定向进化
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
体外
中和抗体
病毒
单域抗体
生物
纳米技术
化学
2019年冠状病毒病(COVID-19)
突变体
生物化学
材料科学
肽序列
遗传学
基因
传染病(医学专业)
酶
疾病
病理
医学
作者
Xun Chen,Matteo Gentili,Nir Hacohen,Aviv Regev
标识
DOI:10.1038/s41467-021-25777-z
摘要
Antibody engineering technologies face increasing demands for speed, reliability and scale. We develop CeVICA, a cell-free nanobody engineering platform that uses ribosome display for in vitro selection of nanobodies from a library of 1011 randomized sequences. We apply CeVICA to engineer nanobodies against the Receptor Binding Domain (RBD) of SARS-CoV-2 spike protein and identify >800 binder families using a computational pipeline based on CDR-directed clustering. Among 38 experimentally-tested families, 30 are true RBD binders and 11 inhibit SARS-CoV-2 pseudotyped virus infection. Affinity maturation and multivalency engineering increase nanobody binding affinity and yield a virus neutralizer with picomolar IC50. Furthermore, the capability of CeVICA for comprehensive binder prediction allows us to validate the fitness of our nanobody library. CeVICA offers an integrated solution for rapid generation of divergent synthetic nanobodies with tunable affinities in vitro and may serve as the basis for automated and highly parallel nanobody engineering.
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