STTR Phase I: COVID-19: AI-based Development of Neutralizing Antibodies for SARS-CoV-2
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202020Known Financial Commitments (USD)
$256,000Funder
National Science Foundation (NSF)Principal Investigator
Barry OlafsonResearch Location
United States of AmericaLead Research Institution
Protabit LLCResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Immunity
Special Interest Tags
Innovation
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The broader impact/commercial potential of this STTR project will lead to the development of engineered antibodies for that can be used to provide passive immunity and treatment to patients infected with COVID-19. These neutralizing antibodies can also be administered as preventative measures for populations at high risk of contracting COVID-19. Such engineered antibodies present a wider range of potential solutions than those produced naturally in the human body, potentially allowing more effective solutions. The proposed combination of high-throughput screening, next-generation-sequencing and AI-based antibody design allows systematic exploration of vast ranges of antibody sequences. This platform technology will be highly responsive to future outbreaks of novel coronaviruses or mutated forms of existing coronaviruses. The technology will be a platform technology which is would be useful going forward for other therapeutics for different diseases beyond coronavirus. Solutions in this space are highly relevant due to the current ongoing COVID-19 pandemic.
This STTR Phase I project proposes to greatly enable AI and machine learning antibody engineering approaches by providing the needed antibody sequence mutation binding data that will take AI-based antibody engineering to a new level. Currently available antibody datasets number in the thousands of datapoints and this project proposes to generate datasets that number in the tens of millions. The project will also be generating both positive and negative antibody binding data, leading to higher performing learned antibody binding models. This project allows testing the hypothesis that synthetic antibodies can be the equal of, or better than, naturally occurring antibodies for neutralizing SARS-CoV-2 infectivity. Nature has its own set of rules and limitations for generating antibodies and the propsoals' approach could potentially develop a much wider range of antibody variations. This work will be laser-focused on discovering a number of high-affinity antibodies targeting the receptor binding domain (RBD) of the SARS-CoV-2 spike protein through the combination of yeast-display, high-throughput FACS sorting and next-generation-sequencing. Combining these high-throughput data generation workflows with the latest deep neural networks will lead to a new methodology that can quickly and efficiently discover high performing antibodies, both for the current pandemic and others that may follow.
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.
This STTR Phase I project proposes to greatly enable AI and machine learning antibody engineering approaches by providing the needed antibody sequence mutation binding data that will take AI-based antibody engineering to a new level. Currently available antibody datasets number in the thousands of datapoints and this project proposes to generate datasets that number in the tens of millions. The project will also be generating both positive and negative antibody binding data, leading to higher performing learned antibody binding models. This project allows testing the hypothesis that synthetic antibodies can be the equal of, or better than, naturally occurring antibodies for neutralizing SARS-CoV-2 infectivity. Nature has its own set of rules and limitations for generating antibodies and the propsoals' approach could potentially develop a much wider range of antibody variations. This work will be laser-focused on discovering a number of high-affinity antibodies targeting the receptor binding domain (RBD) of the SARS-CoV-2 spike protein through the combination of yeast-display, high-throughput FACS sorting and next-generation-sequencing. Combining these high-throughput data generation workflows with the latest deep neural networks will lead to a new methodology that can quickly and efficiently discover high performing antibodies, both for the current pandemic and others that may follow.
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.