Construction of a rationally designed antibody platform for sequencing-assisted selection
Construction of a rationally designed antibody platform for sequencing-assisted selection
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DOI：
10.1073/pnas.1215549109
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发表时间：
2012-11-06
影响因子：
11.1
通讯作者：
Elledge, Stephen J.
中科院分区：
文献类型：
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作者：
Larman, H. Benjamin;Xu, George Jing;Elledge, Stephen J.
抗体发现平台已成为治疗性生物分子和研究试剂的重要来源。大规模并行DNA测序可用于通过在选择期间全面监测文库来辅助抗体选择，从而极大地扩展这些系统的能力。因此，我们构建了一个合理设计的，完全定义的单链可变片段（scFv）文库和分析平台，优化了短读深测序分析。在可编程微阵列上合成编码三个互补决定区（来自轻链的L3，来自重链的H2和H3）的序列限定的寡核苷酸文库，并组合克隆到单个scFv框架中用于分子展示。我们独特的互补决定区序列设计通过利用隐马尔可夫模型优化蛋白质结合，该模型在蛋白质数据库中的所有抗体-抗原共晶结构上进行训练。以核糖体展示形式产生所得的类似于10（12）-成员文库，并通过多重配对末端Illumina测序在四轮抗原选择中进行全面分析。隐马尔可夫模型scFv文库产生了针对新出现的癌症抗原的多种结合剂，并且是下一代抗体生产平台的基础。
Antibody discovery platforms have become an important source of both therapeutic biomolecules and research reagents. Massively parallel DNA sequencing can be used to assist antibody selection by comprehensively monitoring libraries during selection, thus greatly expanding the power of these systems. We have therefore constructed a rationally designed, fully defined single-chain variable fragment (scFv) library and analysis platform optimized for analysis with short-read deep sequencing. Sequence-defined oligonucleotide libraries encoding three complementarity-determining regions (L3 from the light chain, H2 and H3 from the heavy chain) were synthesized on a programmable microarray and combinatorially cloned into a single scFv framework for molecular display. Our unique complementarity-determining region sequence design optimizes for protein binding by utilizing a hidden Markov model that was trained on all antibody-antigen cocrystal structures in the Protein Data Bank. The resultant similar to 10(12)-member library was produced in ribosome-display format, and comprehensively analyzed over four rounds of antigen selections by multiplex paired-end Illumina sequencing. The hidden Markov model scFv library generated multiple binders against an emerging cancer antigen and is the basis for a next-generation antibody production platform.