Quantitative high-throughput methods for antibody fragment optimization and discovery

摘要 单抗和抗体片段是一种重要的治疗方法，耗资1500亿美元 工业。然而，用于发现和优化具有所需亲和力的抗体的方法通常是 费力的实验室程序，需要由高技能人员进行数月的实际研究 人员(如噬菌体展示、杂交瘤、单细胞)。此外，选择要向前推进的潜在客户 治疗开发管道通常必须使用有限的信息，而不一定 对应于定量结合亲和力。为了应对这些挑战，Protillion已将Prot- MAP，一个跨105到109个变异的大型文库测量定量蛋白质结合的平台 自动化仪器，产生结果的时间约为2天。我们实现这一点是通过生成 Illumina DNA直接固定化蛋白原位转录流式细胞测序 还有翻译。该平台允许直接、定量地测量结合到 整个蛋白质文库的规模前所未有--这一规模终于与蛋白质的稀疏性相匹配 在氨基酸突变空间中起作用。在我们的第一阶段，我们采用了prot-map来显示VHH (纳米抗体)能够结合SARS-CoV-2刺突(S1)受体结合域(RBD)蛋白。我们的 多步优化首先全面识别“有益的”突变，然后将这些突变组合成一个 第二个组合库。这一策略确定了数以万计的亲和力优越的蛋白质变体 对于野生型，最好的表现出最高的VHH与该靶标的结合亲和力，100倍 从起点上提升。我们还开发了一种使这个纳米身体人性化的策略，产生了一种 保持高亲和力的近乎完全的人类序列。在第二阶段，我们将首先提高自动化和 我们仪器的商业可扩展性，并为库设计和开发深度学习模型 治疗线索的选择。接下来，我们还将优化其他SARS-CoV-2S1RBD结合纳米体 作为能够结合PD-L1的纳米体，PD-L1是与癌症免疫治疗相关的靶点。我们将开发一种 用于鉴定高亲和力、人源化、临床相关VHH试剂的普遍适用的流水线。我们会 还将我们的显示能力扩展到更大的scFv域，并对两个scFv亲和力进行优化 单独的靶配体，包括SARS-CoV-2 S1 RBD。最后，我们将修改我们的方法以显示多达109个 NovaSeq测序芯片上截然不同的蛋白质变异，这个尺度足以从新发现粘合剂 天真人性化的VHH图书馆。本计划书中概述的活动将支持显示多种类型的 抗体片段，优化亲和力和人源化它们的序列，并清楚地定义 功能蛋白质序列。从非目标文库中从头发现新的活页夹的能力将 使Protilion平台成为垂直集成的“一站式商店”，既可识别来自 非靶向文库，以及对这些变体的详细突变分析和优化。

Abstract Monoclonal antibodies and antibody fragments are an important class of therapeutics comprising a $150B industry. However, methods for discovering and optimizing antibodies to have desired affinity are generally laborious laboratory procedures that require months of hands-on research performed by highly skilled personnel (e.g. phage display, hybridoma, single cell). Additionally, the selection of leads to move forward in the therapeutic development pipeline often must be made with limited information that does not necessarily correspond to quantitative binding affinity. To address these challenges, Protillion has commercialized Prot- MaP, a platform for measuring quantitative protein binding across large libraries of 105 to 109 variants on automated instrumentation, with a time-to-result of approximately 2 days. We achieve this by generating immobilized proteins directly on Illumina DNA sequencing flow cells through a process of in-situ transcription and translation. This platform allows for direct, quantitative measurements of fluorescent antigen binding to entire protein libraries at unprecedented scale—a scale that is finally a match for the sparseness of protein function in amino acid mutation space. In our Phase I period, we adapted Prot-MaP to display VHHs (nanobodies) capable of binding the SARS-CoV-2 spike (S1) receptor binding domain (RBD) protein. Our multi-step optimization first comprehensively identified “beneficial” mutations, which were then combined into a second combinatorial library. This strategy identified tens of thousands of protein variants with affinity superior to wild type, with the best exhibiting the highest reported binding affinity for a VHH to this target, a 100-fold improvement from the starting point. We also developed a strategy to humanize this nanobody, producing a near-fully-human sequence that maintained high affinity. In Phase II, we will first improve automation and commercial scalability of our instrumentation, and develop deep learning models for library design and selection of therapeutic leads. We will next optimize other SARS-CoV-2 S1 RBD-binding nanobodies, as well as nanobodies capable of binding PD-L1, a target relevant to cancer immunotherapy. We will develop a universally applicable pipeline for identifying high-affinity, humanized, clinically-relevant VHH reagents. We will also extend our display capabilities to larger, scFv domains, and carry out scFv affinity optimization against two separate target ligands, including SARS-CoV-2 S1 RBD. Finally, we will adapt our methods to display up to 109 distinct protein variants on a NovaSeq sequencing chip, a scale sufficient to identify binders de novo from naïve humanized VHH libraries. The activities outlined in this proposal will enable display multiple types of antibody fragments, optimize affinity and humanize their sequences, and clearly define the landscape of functional protein sequences. The capability of de novo discovery of new binders from untargeted libraries will make the Protillion platform a vertically integrated “one stop shop” allowing both identification of “hits” from untargeted libraries, as well as detailed mutational analysis and optimization of these variants.