PDF(Pubmed)
Abstract：
Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.
摘要：
在过去的二十年里，治疗性抗体已成为生物制剂领域中快速扩展的结构域。可以简化抗体发现和优化过程的计算机仿真工具对于支持每年越来越多和越来越复杂的管道至关重要。高质量的结构信息对于抗体优化过程仍然至关重要，但是抗体-抗原复合物结构通常无法获得，并且计算机抗体对接方法仍然不可靠。在这项研究中,DeepAb,直接从序列预测抗体Fv结构的深度学习模型，与单点实验深度突变扫描（DMS）富集数据结合使用，以设计抗鸡蛋溶菌酶（HEL）抗体的200种潜在优化变体。我们试图确定含有来自DMS的有益突变的组合的DeepAb设计的变体是否表现出增强的热稳定性,以及这种优化是否影响它们的可显影性概况。通过强大的高通量方法生产了200种变体，并测试了热和胶体稳定性（Tonset，Tm,Tagg),相对于亲本抗体的亲和力（KD），和发育性参数(非特异性结合，聚集倾向，自我关联)。在设计的克隆中，91%和94%表现出增加的热和胶体稳定性和亲和力，分别。其中,10％显示对HEL的亲和力显着增加（增加5至21倍）和热稳定性（Tm1增加>2.5C），大多数克隆保留了亲代抗体的有利发展概况。另外的计算机模拟测试表明,即使没有首先收集实验性DMS测量,这些方法也将富集结合亲和力。这些数据打开了计算机抗体优化的可能性，而无需预测抗体-抗原界面，在没有晶体结构的情况下，这是众所周知的困难。
