Using AI to understand how protein sequences relate to their structures and functions
Official title: Integrative deep learning algorithms for understanding protein sequence-structure-function relationships: representation, prediction, and discovery
Funded at Georgia Institute of Technology · NIH-10919777 · Data updated 2026-11-12
This study is exploring new ways to use artificial intelligence to better understand proteins—how they are built, how they look, and what they do—so that we can make exciting discoveries that could help improve health and technology.
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 | 2024 |
| Award (this year) | $362,878 |
| NIH project number | 5R35GM150890-02 |
| Project ID | NIH-10919777 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for benefiting from this research include individuals with genetic disorders or diseases linked to protein dysfunction.
✕Less likely to help
Patients with conditions unrelated to protein biology or those not affected by protein-related disorders may not receive benefits from this research.
What this research studies
This research focuses on developing advanced artificial intelligence methods to analyze and integrate diverse datasets related to proteins, including their sequences, three-dimensional structures, and functional activities. By leveraging recent breakthroughs in AI, the project aims to uncover the complex relationships between protein sequences, their structures, and their functions. The approach involves creating novel machine learning algorithms that can handle the challenges posed by large and varied datasets, ultimately enhancing our understanding of protein biology. This could lead to new insights that benefit fields like biomedicine and bioengineering.
Why it matters
Potential benefit: If successful, this research could lead to significant advancements in how we understand and manipulate proteins, potentially improving treatments for various diseases.
How similar studies have gone: Other research has shown success in using AI for protein structure prediction, indicating a promising avenue for further exploration in understanding protein functions.
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.