Quantitative high-throughput methods for antibody fragment optimization and discovery

用于抗体片段优化和发现的定量高通量方法

• 批准号: 10325926
• 负责人: Curtis Layton
• 金额: $ 85.53万
• 依托单位: PROTILLION BIOSCIENCES, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325926
• 关键词: 2019-nCoV; Address; Affinity; Amino Acid Sequence; Amino Acids; Antibodies; Antibody Repertoire; Automation; Automobile Driving; Binding; Binding Proteins; Biochemical; Biological Assay; Biophysics; COVID-19; Cells; ChIP-seq; Clinical; Color; DNA biosynthesis; DNA sequencing; Data; Data Set; Detection; Development; Disease; Ecosystem; Evaluation; Exhibits; Fluorescence; Generations; Genes; Genetic Transcription; Human; Human Resources; Hybridomas; Image; Immobilization; Immunoglobulin Fragments; In Situ; In Vitro; Individual; Industry; Infrastructure; Intellectual Property; Laboratory Procedures; Lead; Length; Libraries; Ligands; Liquid substance; Malignant Neoplasms; Maps; Measurement; Measures; Methods; Modeling; Molecular; Monoclonal Antibodies; Mutation; Mutation Analysis; Peptides; Phage Display; Pharmaceutical Preparations; Phase; Process; Property; Protein Array; Protein Binding Domain; Protein Engineering; Protein Microchips; Proteins; Protocols documentation; Randomized; Reagent; Reporting; Runaway; SARS-CoV-2 inhibitor; Specificity; Technology; Therapeutic; Therapeutic antibodies; Time; Transgenic Animals; Translations; Variant; Viral; Work; Yeasts; antigen binding; base; cancer immunotherapy; cellular imaging; clinically relevant; combinatorial; deep learning; design; fighting; hands on research; high throughput analysis; improved; instrument; instrumentation; nanobodies; novel; novel therapeutics; programmed cell death ligand 1; prospective; protein expression; protein function; receptor binding; scaffold; side effect; single cell analysis; success; therapeutic development

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.

抽象的 单克隆抗体和抗体片段是一类重要的治疗方法，包括 $150B 行业。然而，发现和优化抗体以使其具有所需亲和力的方法通常是 费力的实验室程序，需要高技能人员进行数月的实践研究 人员（例如噬菌体展示、杂交瘤、单细胞）。此外，选择要向前推进的线索 治疗开发流程通常必须使用有限的信息来制定，而这些信息不一定 对应于定量结合亲和力。为了应对这些挑战，Protillion 已将 Prot- MaP，一个用于测量包含 105 至 109 个变体的大型文库的定量蛋白质结合的平台 自动化仪器，大约 2 天即可得出结果。我们通过生成来实现这一点 通过原位转录过程将蛋白质直接固定在 Illumina DNA 测序流动槽上 和翻译。该平台允许直接、定量测量荧光抗原结合 整个蛋白质库以前所未有的规模——最终与蛋白质的稀疏性相匹配的规模 氨基酸突变空间中的功能。在第一阶段，我们采用 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”突变，然后将其组合成 第二个组合库。该策略鉴定了数以万计的亲和力优越的蛋白质变体 与野生型相比，最好的 VHH 与该靶点表现出最高的报告结合亲和力，是野生型的 100 倍 从起点开始改进。我们还开发了一种策略来人性化这种纳米抗体，产生 保持高亲和力的接近全人类序列。在第二阶段，我们首先会提高自动化程度， 我们仪器的商业可扩展性，并开发用于库设计和的深度学习模型 治疗线索的选择。接下来我们还将优化其他 SARS-CoV-2 S1 RBD 结合纳米抗体 作为能够结合 PD-L1 的纳米抗体，PD-L1 是与癌症免疫治疗相关的靶标。我们将开发一个 用于鉴定高亲和力、人性化、临床相关的 VHH 试剂的普遍适用的管道。我们将 还将我们的显示能力扩展到更大的 scFv 域，并针对两种情况进行 scFv 亲和力优化 单独的目标配体，包括 SARS-CoV-2 S1 RBD。最后，我们将调整我们的方法以显示最多 109 个 NovaSeq 测序芯片上的不同蛋白质变体，其规模足以从头识别结合物 天真的人性化 VHH 库。本提案中概述的活动将能够显示多种类型的 抗体片段，优化亲和力并将其序列人性化，并清楚地定义了 功能性蛋白质序列。从非目标文库中从头发现新结合物的能力将 使 Protillion 平台成为一个垂直整合的“一站式商店”，允许识别来自 非目标文库，以及这些变体的详细突变分析和优化。