New methods to predict proteins that bind to DNA
Official title: Novel deep learning frameworks for predicting nucleosome-binding proteins
Funded at Rochester Institute of Technology · NIH-11021069 · Data updated 2026-11-12
This study is working on a new way to help scientists quickly and accurately find important proteins that help cells grow and develop, using advanced computer techniques that look at both the proteins' sequences and their shapes.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rochester Institute of Technology NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | March 1, 2024 |
| Project end | February 28, 2026 |
| Funding year | 2025 |
| Award (this year) | $230,330 |
| NIH project number | 5R21GM152740-02 |
| Project ID | NIH-11021069 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with conditions related to cellular differentiation or developmental disorders.
✕Less likely to help
Patients with conditions unrelated to nucleosome-binding proteins or cellular differentiation may not benefit from this research.
What this research studies
This research focuses on developing advanced deep learning techniques to predict nucleosome-binding proteins (NBPs), which are crucial for processes like cell differentiation and organ development. The approach involves a two-stage transfer learning framework that combines information from protein sequences and their three-dimensional structures. By utilizing state-of-the-art Graph Neural Networks, the researchers aim to enhance the accuracy and efficiency of identifying these proteins, which is currently a costly and time-consuming process. This innovative methodology could lead to significant advancements in understanding cellular functions and development.
Why it matters
Potential benefit: If successful, this research could provide a faster and more cost-effective way to identify proteins that play key roles in cell development and differentiation.
How similar studies have gone: Other research has shown promise in using deep learning for protein prediction, indicating that this approach could be effective.
Where this research is happening
-
Rochester Institute of Technology
Rochester, United States Active
Researchers
- Principal investigator: Cui, Feng — Rochester Institute of Technology
- Study coordinator: Cui, Feng
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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.