蛋白质结合剂的治疗功效很大程度上取决于两个因素:其结合位点和其结合亲和力。体外文库展示筛选和下一代测序的进展使得强结合剂的开发加速,然而,确定它们的结合位点仍然是一个重大挑战。分化,或“装仓”,将结合物整合到识别其靶标上的不同结合位点的不同基团中是一种有希望的方法,其促进可显示不同生物活性的结合物的高通量筛选。在这里,我们研究了包含一组靶标特异性结合物的氨基酸序列中包含的信息可以被利用来分类它们的程度,推断其结合区域的功能等效性,或者逆位,直接基于序列的比较,他们的建模结构,或它们的建模相互作用。使用称为“排斥体”的富含亮氨酸的重复结合支架作为识别白介素6(IL-6)的多样性来源,我们表明,此处引入的“Epibin”方法有效地利用了结构建模和对接来提取重复体氨基酸序列中编码的特异性信息,从而成功地概括了免疫测定中观察到的IL-6结合竞争。此外,我们的计算分类为设计体外诱变实验以确定特异性决定残基提供了基础。最后,我们证明了Epibin方法可以扩展到抗体,回顾性地将其预测与抗原特异性抗体竞争研究的结果进行比较。因此,该研究证明了模拟结构和从不同结合物的氨基酸序列针对相同靶标的结合的实用性。并为整个剧目的大规模分类和分析铺平了道路。
The therapeutic efficacy of a protein binder largely depends on two factors: its binding site and its binding affinity. Advances in in vitro library display screening and next-generation sequencing have enabled accelerated development of strong binders, yet identifying their binding sites still remains a major challenge. The differentiation, or \"binning\", of binders into different groups that recognize distinct binding sites on their target is a promising approach that facilitates high-throughput screening of binders that may show different biological activity. Here we study the extent to which the information contained in the amino acid sequences comprising a set of target-specific binders can be leveraged to bin them, inferring functional equivalence of their binding regions, or paratopes, based directly on comparison of the sequences, their modeled structures, or their modeled interactions. Using a leucine-rich repeat binding scaffold known as a \"repebody\" as the source of diversity in recognition against interleukin-6 (IL-6), we show that the \"Epibin\" approach introduced here effectively utilized structural modelling and docking to extract specificity information encoded in the repebody amino acid sequences and thereby successfully recapitulate IL-6 binding competition observed in immunoassays. Furthermore, our computational binning provided a basis for designing in vitro mutagenesis experiments to pinpoint specificity-determining residues. Finally, we demonstrate that the Epibin approach can extend to antibodies, retrospectively comparing its predictions to results from antigen-specific antibody competition studies. The study thus demonstrates the utility of modeling structure and binding from the amino acid sequences of different binders against the same target, and paves the way for larger-scale binning and analysis of entire repertoires.