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.

抗体工程技术面临着对速度、可靠性和规模日益增长的要求。我们开发了 CeVICA，这是一个无细胞纳米抗体工程平台，利用核糖体展示从 10 ¹¹个随机序列文库中体外选择纳米抗体。我们应用 CeVICA 设计针对 SARS-CoV-2 刺突蛋白受体结合域 (RBD) 的纳米抗体，并使用基于 CDR 引导聚类的计算管道识别 >800 结合物家族。在 38 个经过实验测试的家族中，30 个是真正的 RBD 结合剂，11 个抑制 SARS-CoV-2 假型病毒感染。亲和力成熟和多价工程增加了纳米抗体的结合亲和力并产生了具有皮摩尔 IC50 的病毒中和剂。此外，CeVICA 的综合结合预测能力使我们能够验证纳米抗体库的适用性。 CeVICA 提供了一种集成解决方案，可在体外快速生成具有可调亲和力的不同合成纳米抗体，并可作为自动化和高度并行纳米抗体工程的基础。