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Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design

机译:使用基于结构的计算设计提高体内成熟的治疗性抗体的亲和力

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摘要

Improving the affinity of a high-affinity protein–protein interaction is a challenging problem that has practical applications in the development of therapeutic biomolecules. We used a combination of structure-based computational methods to optimize the binding affinity of an antibody fragment to the I-domain of the integrin VLA1. Despite the already high affinity of the antibody (Kd ∼7 nM) and the moderate resolution (2.8 Å) of the starting crystal structure, the affinity was increased by an order of magnitude primarily through a decrease in the dissociation rate. We determined the crystal structure of a high-affinity quadruple mutant complex at 2.2 Å. The structure shows that the design makes the predicted contacts. Structural evidence and mutagenesis experiments that probe a hydrogen bond network illustrate the importance of satisfying hydrogen bonding requirements while seeking higher-affinity mutations. The large and diverse set of interface mutations allowed refinement of the mutant binding affinity prediction protocol and improvement of the single-mutant success rate. Our results indicate that structure-based computational design can be successfully applied to further improve the binding of high-affinity antibodies.
机译:提高高亲和力蛋白-蛋白相互作用的亲和力是一个具有挑战性的问题,在治疗性生物分子的开发中具有实际应用。我们使用基于结构的计算方法的组合来优化抗体片段与整联蛋白VLA1的I结构域的结合亲和力。尽管抗体已经具有很高的亲和力(Kd〜7 nM)和起始晶体结构的中等分辨率(2.8Å),但亲和力主要通过解离速率的降低而增加了一个数量级。我们确定了高亲和力四重突变体复合物在2.2Å处的晶体结构。该结构表明该设计可以预测联系人。探测氢键网络的结构证据和诱变实验说明了在寻求更高亲和力突变时满足氢键要求的重要性。大量多样的界面突变允许改进突变体结合亲和力预测方案并提高单突变体成功率。我们的结果表明,基于结构的计算设计可以成功地应用于进一步改善高亲和力抗体的结合。

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