Table2_AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study.xlsx

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.

引言：本研究验证了酵母表面展示（yeast surface display, YSD）与下一代测序（next-generation sequencing, NGS）结合人工智能与机器学习方法（AI/ML），用于鉴定具备优良早期开发特性的从头人源化单域抗体（single domain antibodies, sdAbs）的可行性。
方法：本次展示文库源自一种全新策略：将经NKp46免疫的美洲驼（Lama glama）来源的、基于VHH（variable domain of heavy chain antibody）的CDR3区域，嫁接至经CDR1与CDR2区域多样化改造的人源化VHH骨架文库中。在对两轮荧光激活细胞分选获得的序列库进行NGS分析后，我们基于NGS测序频率、富集分析以及虚拟可开发性评估，筛选出4个序列簇。针对每个序列簇，我们训练了基于长短期记忆网络（long short-term memory, LSTM）的深度生成模型，并将其用于新序列的虚拟采样。随后对所有序列进行基于序列与结构的虚拟可开发性评估，从每个序列簇中筛选出不足10条序列用于后续制备。
结果：通过结合动力学与早期开发特性评估验证，本流程可作为一种通用策略，能够从具备优良早期开发特性的筛选体系中，快速高效地设计得到高活性且全自动人源化的单域抗体命中克隆。