The ability of antibodies to recognize their target antigens with high specificity is fundamental to their natural function. Nevertheless, therapeutic antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that can lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and rapid antibody clearance. Here we report a method for predicting the overall specificity of antibodies in terms of their relative risk for displaying high levels of nonspecific or self-interactions at physiological conditions. We find that individual and combined sets of chemical rules that limit the maximum and minimum numbers of certain solvent-exposed amino acids in antibody variable regions are strong predictors of specificity for large panels of preclinical and clinical-stage antibodies. We also demonstrate how the chemical rules can be used to identify sites that mediate nonspecific interactions in suboptimal antibodies and guide the design of targeted sublibraries that yield variants with high antibody specificity. These findings can be readily used to improve the selection and engineering of antibodies with drug-like specificity.

中文翻译：

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鉴定具有药物样特异性的单克隆抗体的理化规则。

抗体以高特异性识别其靶抗原的能力是其天然功能的基础。然而，治疗性抗体表现出可变且难以预测的非特异性和自身相互作用水平，这可能导致各种药物开发挑战，包括抗体聚集、异常高粘度和快速抗体清除。在这里，我们报告了一种根据抗体在生理条件下表现出高水平非特异性或自我相互作用的相对风险来预测抗体总体特异性的方法。我们发现，限制抗体可变区中某些暴露于溶剂的氨基酸的最大和最小数量的单独和组合的化学规则是大量临床前和临床阶段抗体的特异性的强有力的预测因子。我们还演示了如何使用化学规则来识别介导次优抗体中非特异性相互作用的位点，并指导设计产生具有高抗体特异性的变体的目标子库。这些发现可以很容易地用于改进具有药物样特异性的抗体的选择和工程设计。