PDF(Pubmed)
Abstract：
Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.
摘要：
引言：在这项研究中，我们证明了酵母表面展示（YSD）和下一代测序（NGS）结合人工智能和机器学习方法（AI/ML）的可行性，用于鉴定具有良好早期可开发性的从头人源化单结构域抗体（sdAb）。方法：展示库来自一种新颖的方法，其中基于VHH的CDR3区域从美洲驼（Lamaglama）获得，针对NKp46免疫,将其移植到在CDR1和CDR2中多样化的人源化VHH骨架文库上。在来自两轮荧光激活细胞分选的序列池的NGS分析之后,我们基于NGS频率和富集分析以及计算机可显影性评估关注四个序列簇。对于每个集群，训练了基于长短期记忆(LSTM)的深度生成模型,并将其用于新序列的计算机模拟采样.对序列进行基于序列和结构的计算机可显影性评估,以选择一组每个簇少于10个序列用于生产。结果：如结合动力学和早期显影性评估所示，该程序代表了从筛选选择中快速有效地设计强效且自动人源化的sdAb命中物的一般策略,该筛选选择具有良好的早期发展概况.
