Improving the stability and function of proteins using AI and experimental testing
Official title: Engineering Enzymes for Improved Stability and Retained Function via Rapid Design-Build-Test-Learn Cycles integrating AI/Physics based predictions with Cell-free Protein Synthesis Experimental Testing
Funded at Brigham Young University · NIH-10973759 · Data updated 2026-11-12
This study is exploring new ways to make important proteins used in cancer tests and treatments more stable and effective, using smart computer techniques to help design and test them quickly.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Brigham Young University NIH-funded |
| Lab location | 1 site (Provo, United States) |
| Project start | August 1, 2024 |
| Project end | July 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $448,723 |
| NIH project number | 1R15GM155803-01 |
| Project ID | NIH-10973759 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with cancers that could benefit from improved therapeutic proteins or those requiring advanced diagnostic methods.
✕Less likely to help
Patients who do not have cancer or do not require protein-based therapies or diagnostics may not benefit from this research.
What this research studies
This research focuses on enhancing the stability and functionality of therapeutic and diagnostic proteins by utilizing advanced AI techniques and experimental methods. By integrating AI-based predictions with cell-free protein synthesis, the project aims to rapidly design, build, test, and learn from various protein sequences. The goal is to identify mutations that improve protein stability while retaining their essential functions, specifically targeting proteins like NanoLuc and Onconase that are crucial for cancer diagnostics and treatment.
Why it matters
Potential benefit: If successful, this research could lead to more effective and stable cancer therapies and diagnostic tools for patients.
How similar studies have gone: Other research has shown success in using AI for protein engineering, indicating that this approach has potential for significant advancements.
Where this research is happening
-
Brigham Young University
Provo, United States Active
Researchers
- Principal investigator: Della Corte, Dennis — Brigham Young University
- Study coordinator: Della Corte, Dennis
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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.