STTR Phase II: Artificial Intelligence (AI)-based Development of Neutralizing Antibodies for SARS-CoV-2

STTR 第二阶段：基于人工智能 (AI) 的 SARS-CoV-2 中和抗体开发

• 批准号: 2136860
• 负责人: Barry Olafson
• 金额: $ 100万
• 依托单位: Protabit LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136860&HistoricalAwards=false
• 关键词: STTR; Phase; II; Artificial; Intelligence

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project will lead to the development of engineered neutralizing antibodies for the SARS-CoV-2 virus that can be used as therapeutic agents to diminish the severity of a COVID-19 infection and decrease the chances of hospitalization and progressive disease. As the SARS-CoV-2 virus continues to mutate it is necessary to increase collective preparedness by generating a wide collection of neutralizing antibodies that individually provide coverage for a range of variants (Delta, Beta, Omicron). These antibodies, when administered as an antibody cocktail, may offer broad protection over a diverse population of variants. This project’s approach generates neutralizing antibodies that are specifically engineered to bind to different regions of the spike protein thereby increasing the probability that one or more of the engineered antibodies will be effective against future mutated versions of the virus. The proposed combination of high-throughput screening, next-generation-sequencing and artificial intelligence (AI)-based antibody design allows systematic exploration of vast ranges of antibody sequences. This approach also has the benefit of engineering antibodies that are more potent, easier to administer, more stable under challenging environmental conditions, and less costly to manufacture, leading to therapeutics that can be more readily distributed to low-income countries.This STTR Phase II project proposes to enable AI and machine learning antibody engineering approaches by providing needed antibody sequence mutation binding data. Currently available antibody datasets number in the thousands of datapoints and the team proposes to generate datasets that number in the tens of millions. The project will also be generating both positive and negative antibody binding data, potentially leading to higher performing learned antibody binding models. This project seeks to test the hypothesis that synthetic antibodies can be the equal of, or better than, naturally occurring antibodies for neutralizing SARS-CoV-2 infectivity. This approach could potentially develop a wide range of antibody variations. The application of this AI-based antibody engineering will be focused on discovering a large array of high-affinity neutralizing antibodies targeting multiple, different regions of the SARS-CoV-2 spike protein through the combination of yeast-display, high-throughput fluorescence-activated cell sorting (FACS) and next generation sequencing. Combining these high-throughput data generation workflows with the latest deep neural networks may lead to a new methodology that can efficiently discover high performing antibodies for the current pandemic and those in the future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究（SBIR）项目的更广泛影响/商业潜力将导致SARS-CoV-2病毒的工程中和抗体的开发，可用作治疗剂，以减轻COVID-19感染的严重程度，并减少住院和疾病进展的机会。随着SARS-CoV-2病毒继续变异，有必要通过产生广泛的中和抗体来增加集体准备，这些抗体单独为一系列变体（Delta，Beta，Omicron）提供覆盖。当作为抗体混合物施用时，这些抗体可以对不同的变体群体提供广泛的保护。该项目的方法产生了中和抗体，这些抗体被专门设计为与刺突蛋白的不同区域结合，从而增加了一种或多种工程抗体对未来病毒突变版本有效的可能性。所提出的高通量筛选、下一代测序和基于人工智能（AI）的抗体设计的组合允许系统地探索各种抗体序列。这种方法还具有工程抗体的好处，这些抗体更有效，更容易管理，在具有挑战性的环境条件下更稳定，并且生产成本更低，从而使治疗药物更容易分配到低收入国家。这个STTR第二阶段项目提出通过提供所需的抗体序列突变结合数据来实现AI和机器学习抗体工程方法。目前可用的抗体数据集有数千个数据点，该团队建议生成数千万个数据集。该项目还将生成阳性和阴性抗体结合数据，可能导致更高性能的学习抗体结合模型。该项目旨在测试合成抗体在中和SARS-CoV-2感染性方面可以等于或优于天然抗体的假设。这种方法可能会产生广泛的抗体变异。这种基于人工智能的抗体工程的应用将集中在通过酵母展示，高通量荧光激活细胞分选（FACS）和下一代测序的组合，发现大量针对SARS-CoV-2刺突蛋白的多个不同区域的高亲和力中和抗体。将这些高通量数据生成工作流程与最新的深度神经网络相结合，可能会产生一种新的方法，可以有效地发现当前和未来大流行的高性能抗体。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。