Reduction of therapeutic antibody self-association using yeast-display selections and machine learning.
Reduction of therapeutic antibody self-association using yeast-display selections and machine learning.
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DOI：
10.1080/19420862.2022.2146629
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发表时间：
2022-01
期刊：
影响因子：
5.3
通讯作者：
Tessier, Peter M.
中科院分区：
文献类型：
--
作者：
Makowski, Emily K.;Chen, Hongwei;Lambert, Matthew;Bennett, Eric M.;Eschmann, Nicole S.;Zhang, Yulei;Zupancic, Jennifer M.;Desai, Alec A.;Smith, Matthew D.;Lou, Wenjia;Fernando, Amendra;Tully, Timothy;Gallo, Christopher J.;Lin, Laura;Tessier, Peter M.
关键词：
在用于皮下递送的高浓度配方中，自关联控制着治疗性抗体的粘度和溶解度，但在抗体发现和早期优化过程中，很难可靠地识别低自关联的候选抗体。在这里，我们报告了一种高通量蛋白质工程方法，用于快速鉴定具有低自结合和高亲和力的候选抗体。我们发现，将量子点偶联到高度自关联的igg (pH值7.4，PBS)，如lenzilumab和bococizumab，产生的免疫偶联物对检测其他高自关联抗体高度敏感。此外，这些偶联物可用于快速丰富酵母显示的bococizumab亚库，用于具有低水平免疫偶联物结合的变体。对富集的bococizumab文库进行深度测序和机器学习分析，以及类似的抗体亲和力文库分析，能够鉴定出具有共同优化的低自我关联和高亲和力水平的极其罕见的变异。该分析显示，共同优化bococizumab是困难的，因为大多数高亲和变异具有带正电荷的可变结构域，而大多数低自关联变异具有带负电荷的可变结构域。此外，bococizumab重链CDR2中的负电荷突变，邻近其paratope，可以有效地减少自我结合而不降低亲和力。有趣的是，大多数自我关联降低的bococizumab变异体也显示出改善的折叠稳定性和减少的非特异性结合，这表明这种方法可能对识别具有药物样特性的有吸引力的组合的候选抗体特别有用。AC-SINS：亲和捕获自相互作用纳米粒子光谱；CDR：互补决定区；CS-SINS：电荷稳定自相互作用纳米粒子光谱；FACS：荧光活化细胞分选；Fab：片段抗原结合；Fv：片段变量；免疫球蛋白:免疫球蛋白;QD：量子点；PBS：磷酸盐缓冲盐水；VH：可变重；VL：可变光。
Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties. Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; VH: variable heavy; VL: variable light.
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