Predicting antibody sequences to fight new viruses quickly
Official title: Rapid response for pandemics: single cell sequencing and deep learning to predict antibody sequences against an emerging antigen
Funded at Keck Graduate Inst of Applied Life Scis · NIH-10274223 · Data updated 2026-11-12
This study is working on a quick way to find new antibodies that can fight off viruses, which could help create treatments faster during outbreaks, making it easier for patients to get the care they need.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Keck Graduate Inst of Applied Life Scis NIH-funded |
| Lab location | 1 site (Claremont, United States) |
| Project start | September 16, 2021 |
| Project end | August 31, 2025 |
| Funding year | 2021 |
| Award (this year) | $1,851,627 |
| NIH project number | 1R01AI169543-01 |
| Project ID | NIH-10274223 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who may be at risk for infections from newly emerging viruses.
✕Less likely to help
Patients with chronic conditions unrelated to viral infections may not receive direct benefits from this research.
What this research studies
This research focuses on developing a method to rapidly predict antibody sequences that can effectively bind to new viral proteins. By utilizing advanced deep learning techniques and analyzing structural features of antigens, the project aims to generate potential antibody candidates within 24 hours of identifying a new virus. Patients may benefit from this research as it could lead to faster development of effective treatments during pandemics. The approach involves extensive computational modeling and data analysis to enhance our understanding of immune responses.
Why it matters
Potential benefit: If successful, this research could significantly speed up the development of effective antibody treatments for emerging viral infections.
How similar studies have gone: Other research has shown promise in using computational methods for antibody prediction, but this specific approach is innovative and largely untested.
Where this research is happening
-
Keck Graduate Inst of Applied Life Scis
Claremont, United States Active
Researchers
- Principal investigator: Ray, Animesh — Keck Graduate Inst of Applied Life Scis
- Study coordinator: Ray, Animesh
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.