Integrative AI methods to map protein sequences to structure and function
Official title: Integrative deep learning algorithms for understanding protein sequence-structure-function relationships: representation, prediction, and discovery
Funded at Georgia Institute of Technology · NIH-11384669 · Data updated 2026-11-12
A scientist at Georgia Tech is using advanced AI to understand how protein sequences lead to their 3D shapes and functions, which could speed up biomedicine and bioengineering.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Georgia Institute of Technology NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | September 5, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $362,878 |
| NIH project number | 5R35GM150890-04 |
| Project ID | NIH-11384669 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are researchers and clinicians who work with proteins and rely on protein design or disease mechanism studies; no direct patient participation is required.
✕Less likely to help
Patients would not directly participate in this grant, and there is no immediate clinical trial component.
What this research studies
This project develops new machine learning tools to combine diverse protein data like sequences, 3D structures, and functional measurements. It aims to reveal how changes in sequence affect structure and activity by integrating heterogeneous datasets. The work builds on recent AI advances to move beyond structure prediction toward a holistic view of protein biology and function. Results could inform better protein design and understanding of diseases at the molecular level. The research is led by Yunan Luo at Georgia Tech and focuses on scalable AI approaches for complex biological data.
Why it matters
Potential benefit: If successful, the work could accelerate protein design, drug development, and biomedical discovery by clarifying how sequence changes alter structure and function.
How similar studies have gone: Previous AI-driven protein analysis has shown promise, and integrating diverse data could yield deeper insights beyond existing methods.
Where this research is happening
-
Georgia Institute of Technology
Atlanta, United States Active
Researchers
- Principal investigator: Luo, Yunan — Georgia Institute of Technology
- Study coordinator: Luo, Yunan
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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.