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From the Cover: Construction of a rationally designed antibody platform for sequencing-assisted selection

机译:从封面开始:构建用于序列辅助选择的合理设计的抗体平台

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摘要

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 ∼1012-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.
机译: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过程中全面监控文库来辅助抗体选择,从而大大扩展了这些系统的功能。因此,我们构建了一个经过合理设计,完全定义的单链可变片段(scFv)库和分析平台,该平台针对短读深测序进行了优化分析。在可编程微阵列上合成编码三个互补决定区(轻链的L3,重链的H2和H3)的序列定义的寡核苷酸文库,并组合克隆到单个scFv框架中进行分子展示。我们独特的互补决定区序列设计通过利用蛋白质数据库中所有抗体-抗原共晶体结构上受过训练的隐马尔可夫模型优化了蛋白质结合。产生的〜10 12 成员文库以核糖体展示形式产生,并通过多对末端Illumina测序对四轮抗原选择进行了全面分析。隐藏的Markov模型scFv库产生了针对新兴癌症抗原的多种结合物,是下一代抗体生产平台的基础。

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