Integrative AI tools to map protein sequences to 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-11534135 · Data updated 2026-11-12
A scientist team will develop advanced AI methods to better connect protein sequences, their 3D shapes, and how they work, using large biological data from sequencing and structures.
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) | $190,000 |
| NIH project number | 3R35GM150890-04S1 |
| Project ID | NIH-11534135 on NIH RePORTER |
Who could benefit from this research
✓Could help
This is a basic science grant; direct patient participation is not required.
✕Less likely to help
People without an interest in protein science or AI method development would not be directly impacted.
What this research studies
This project from Georgia Tech aims to create new AI methods that integrate diverse protein data, helping us understand how a protein’s sequence leads to its 3D form and its function. By handling noisy, high-dimensional datasets, the work seeks to reveal mechanistic links that are hard to discern today. The team plans to push beyond existing predictors and develop holistic models that analyze sequence, structure, and activity together. The research could provide new insights for protein biology, biomedicine, and bioengineering, using state-of-the-art AI advances as a foundation. It is led by Yun an Luo at Georgia Tech and is funded through a substantial NIH grant trial to advance protein science.
Why it matters
Potential benefit: If successful, this research could accelerate discovery of proteins with useful functions and improve design of proteins for therapies and biotechnology.
How similar studies have gone: Previous AI-driven protein studies have shown promise, with ongoing work expanding from structure prediction to functional understanding.
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
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
-
Global Deep Learning effort to predict depression treatment response
A patient-focused project uses AI on brain scans to predict who with major depression will respond to rTMS treatment.
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.