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: 10454415
• 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/10454415
• 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; 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; deep learning model; 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，汇编了$ 150B 行业。但是，发现和优化具有所需亲和力的抗体的方法通常是 实验室程序需要数月的动手研究 人员（例如噬菌体显示，杂交瘤，单细胞）。此外，选择线索前进 通常必须使用有限的信息来制作治疗开发管道 对应于定量结合亲和力。为了应对这些挑战，普利利已经商业化了 MAP，一个用于测量105至109个变体的大型文库中定量蛋白结合的平台 自动化仪器，大约2天的时间时间。我们通过产生 通过原位转录的过程，直接在Illumina DNA测序流中直接在Illumina DNA测序流中 和翻译。该平台允许对荧光抗原结合的直接定量测量 整个蛋白质文库以前所未有的规模 - 最终与蛋白质稀疏相匹配 在氨基酸突变空间中的功能。在我们的第一阶段时期，我们调整了Prot-Map以显示VHHS （纳米化）能够结合SARS-COV-2尖峰（S1）受体结合结构域（RBD）蛋白。我们的 多步优化首先全面鉴定出“有益”突变，然后将其合并为 第二组合库。该策略确定了数以万计的蛋白质变体具有优势 对野生型，最好的vHH对此目标表现出最高的结合亲和力，100倍 起点的改进。我们还制定了一种人性化这种纳米僵局的策略，产生了 保持高亲和力的近乎人类序列。在第二阶段，我们将首先改善自动化和 我们的仪器的商业可扩展性，并为图书馆设计开发深度学习模型和 选择治疗铅。接下来，我们还将优化其他SARS-COV-2 S1 RBD结合纳米组合 作为能够结合PD-L1的纳米剂，与癌症免疫疗法相关的靶标。我们将发展一个 普遍适用的管道，用于识别高亲和力，人源化的临床含量VHH试剂。我们将 还将我们的显示功能扩展到较大的SCFV域，并对两个 单独的靶配体，包括SARS-COV-2 S1 RBD。最后，我们将调整我们的方法最多显示109 Novaseq测序芯片上的独特蛋白质变体，这是一个足以识别从头开始的量表 幼稚的人源化VHH图书馆。该提案中概述的活动将使显示多种类型的 抗体片段，优化亲和力并使其序列人性化，并清楚地定义了 功能蛋白序列。从从头开始发现的新粘合剂的能力将 使Protillion平台成为垂直集成的“一站式商店”，允许从 未定位的库，以及这些变体的详细突变分析和优化。