Deschaght Pieter, Vintém Ana Paula, Logghe Marc, Conde Miguel, Felix David, Mensink Rob, Gonçalves Juliana, Audiens Jorn, Bruynooghe Yanik, Figueiredo Rita, Ramos Diana, Tanghe Robbe, Teixeira Daniela, Van de Ven Liesbeth, Stortelers Catelijne, Dombrecht Bruno
Ablynx N.V., Ghent, Belgium.
Front Immunol. 2017 Apr 10;8:420. doi: 10.3389/fimmu.2017.00420. eCollection 2017.
Next-generation sequencing (NGS) has been applied successfully to the field of therapeutic antibody discovery, often outperforming conventional screening campaigns which tend to identify only the more abundant selective antibody sequences. We used NGS to mine the functional nanobody repertoire from a phage-displayed camelid immune library directed to the recepteur d'origine nantais (RON) receptor kinase. Challenges to this application of NGS include accurate removal of read errors, correct identification of related sequences, and establishing meaningful inclusion criteria for sequences-of-interest. To this end, a sequence identity threshold was defined to separate unrelated full-length sequence clusters by exploring a large diverse set of publicly available nanobody sequences. When combined with majority-rule consensus building, applying this elegant clustering approach to the NGS data set revealed a wealth of >5,000-enriched candidate RON binders. The huge binding potential predicted by the NGS approach was explored through a set of randomly selected candidates: 90% were confirmed as RON binders, 50% of which functionally blocked RON in an ERK phosphorylation assay. Additional validation came from the correct prediction of all 35 RON binding nanobodies which were identified by a conventional screening campaign of the same immune library. More detailed characterization of a subset of RON binders revealed excellent functional potencies and a promising epitope diversity. In summary, our approach exposes the functional diversity and quality of the outbred camelid heavy chain-only immune response and confirms the power of NGS to identify large numbers of promising nanobodies.
下一代测序（NGS）已成功应用于治疗性抗体发现领域，其表现往往优于传统筛选方法，传统方法往往只能识别出更丰富的选择性抗体序列。我们使用NGS从针对南特起源受体（RON）受体激酶的噬菌体展示骆驼科动物免疫文库中挖掘功能性纳米抗体库。NGS在该应用中的挑战包括准确去除读取错误、正确识别相关序列以及为感兴趣的序列建立有意义的纳入标准。为此，通过探索大量多样的公开可用纳米抗体序列，定义了一个序列同一性阈值，以分离不相关的全长序列簇。当与多数规则一致性构建相结合时，将这种巧妙的聚类方法应用于NGS数据集，揭示了大量超过5000个富集的候选RON结合物。通过一组随机选择的候选物探索了NGS方法预测的巨大结合潜力：90%被确认为RON结合物，其中50%在ERK磷酸化测定中功能性阻断了RON。额外的验证来自于对通过同一免疫文库的传统筛选鉴定出的所有35个RON结合纳米抗体的正确预测。对一部分RON结合物进行更详细的表征，揭示了优异的功能效力和有前景的表位多样性。总之，我们的方法揭示了远交骆驼科动物仅重链免疫反应的功能多样性和质量，并证实了NGS识别大量有前景的纳米抗体的能力。
