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.

该小企业创新研究 (SBIR) 项目的更广泛影响/商业潜力将导致针对 SARS-CoV-2 病毒的工程化中和抗体的开发，该抗体可用作治疗剂以减轻 COVID-19 感染的严重程度随着 SARS-CoV-2 病毒持续变异，有必要通过产生广泛的中和抗体来加强集体准备，这些抗体可单独覆盖一系列变体（Delta、Beta、这些抗体作为抗体混合物施用时，可以对多种变体提供广泛的保护，该项目的方法产生了经过专门设计以结合刺突蛋白不同区域的中和抗体，从而增加了一种或多种变体的可能性。更多的工程抗体将有效对抗未来的病毒突变版本。所提议的高通量筛选、下一代测序和基于人工智能（AI）的抗体设计相结合，可以系统地探索大量的抗体序列。这种方法还具有工程化抗体的好处是更有效、更容易管理、在具有挑战性的环境条件下更稳定、制造成本更低，从而使治疗方法更容易分发到低收入国家。这个 STTR 第二阶段项目旨在使人工智能成为可能通过提供所需的抗体序列突变结合数据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。抗体结合数据，可能导致更高性能的学习该项目旨在测试以下假设：合成抗体在中和 SARS-CoV-2 感染方面可以等同于或优于天然抗体，这种方法可能会开发出广泛的抗体变体应用。这种基于人工智能的抗体工程的重点是通过酵母展示、高通量荧光激活细胞的结合，发现针对 SARS-CoV-2 刺突蛋白多个不同区域的大量高亲和力中和抗体分选（FACS）和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，可以有效地发现针对当前流行病和未来流行病的高性能抗体。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。