SBIR Phase I: Developing a Rapid Antibody Generation Platform for Emerging COVID-19 Variants

这个小企业创新研究第一阶段项目的更广泛影响是改进和加快抗体疗法的开发，以抗击新冠肺炎大流行。针对新冠肺炎的SARS-CoV-2病毒的抗体疗法正在开发中，但新出现的病毒突变可能会导致耐药性，需要开发新的疗法。抗体疗法通常在开发一年或更长时间后进入临床，全世界都严重感受到了这种延迟的医疗和经济后果。为了减少产生针对新出现的SARS-CoV-2突变株的抗体的时间，该项目开发了一个使用大量实验数据集训练的计算平台，以快速预测治疗效果。这一平台将使针对SARS-CoV-2突变株的药物更快地进入临床，拯救数千人的生命。此外，拟议的平台可用于预测对未来与新冠肺炎大流行无关的冠状病毒株的治疗效果，为抗击未来的流行病提供了宝贵的工具。拟议的项目将展示使用定量和库上蛋白质相互作用数据集来训练机器学习模型的可行性，以预测抗体与新的SARS-CoV-2变异株的结合。现有的为抗体药物开发建立计算预测的方法仅限于少数目标变体，因为没有针对数百或数千个目标的具有约束性测量的数据集。该项目包括优化和验证一个基于细胞的平台，以生成足够数量和质量的抗体-抗原结合数据，用于训练计算模型。该平台使用基因工程酵母细胞和下一代测序将蛋白质相互作用强度与细胞交配频率联系起来。为了证明可行性，大型多链抗体库将在酵母中进行基因组整合，并浓缩以与SARS-CoV-2和相关冠状病毒结合。接下来，将测量抗体-抗原相互作用的大型网络，并通过与生物物理测量进行比较来验证其定量准确性。最后，生成的数据将用于训练机器学习模型，并使用交叉验证来评估其预测能力。对具有足够预测能力的计算模型的培训将展示使用定量和库上库结合数据与机器学习相结合的可行性，以开发针对新型SARS-CoV-2变异株的快速抗体开发平台。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve and accelerate the development of antibody therapies to fight the COVID-19 pandemic. Antibody therapies are in development to target SARS-CoV-2, the virus responsible for COVID-19, but emerging virus mutations may result in resistance and require the development of new therapies. Antibody therapies typically enter the clinic after a year of development or longer, and the medical and economic consequences of this delay are severely felt throughout the world. To reduce the time to produce an antibody against an emerging SARS-CoV-2 mutant, this project develops a computational platform trained using massive experimental datasets to rapidly predict the therapeutic potency. This platform will enable drugs against SARS-CoV-2 mutants to more rapidly reach the clinic, saving thousands of lives. Moreover, the proposed platform can be utilized to predict therapeutic efficacy against future coronavirus strains unassociated with the COVID-19 pandemic, providing an invaluable tool to fight future pandemics.The proposed project will demonstrate the feasibility of using quantitative and library-on-library protein interaction datasets to train machine learning models for predicting antibody binding to novel SARS-CoV-2 variants. Existing approaches to build computational predictions for antibody drug development have been limited to few target variants, since datasets with binding measurements against hundreds or thousands of targets are not available. This project involves optimizing and validating a cell-based platform for generating a sufficient quantity and quality of antibody-antigen binding data for training computational models. The platform uses genetically engineered yeast cells and next generation sequencing to link protein interaction strength with cellular mating frequency. To demonstrate feasibility, large multi-chain antibody libraries will be genomically integrated in yeast and enriched for binding to SARS-CoV-2 and related coronaviruses. Next, a large network of antibody-antigen interactions will be measured and validated for quantitative accuracy by comparing to biophysical measurements. Finally, the resulting data will be used to train machine learning models and evaluate their predictive power using cross-validation. Training of computational models with sufficient predictive power will demonstrate the feasibility of using quantitative and library-on-library binding data coupled with machine learning to develop a platform for rapid antibody development to a novel SARS-CoV-2 mutant.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.