Improving predictions of how genetic changes affect proteins
Official title: Extending the utility and performance of variant effect predictors with protein language models
Funded at University of California, San Francisco · NIH-11048971 · Data updated 2026-11-12
This study is looking at how changes in the building blocks of proteins can affect their function, using smart computer models to help doctors better understand genetic variations that might impact your health, so you can get clearer answers about your genetic conditions.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | January 6, 2025 |
| Project end | December 31, 2029 |
| Funding year | 2025 |
| Award (this year) | $687,063 |
| NIH project number | 1R01HG013524-01A1 |
| Project ID | NIH-11048971 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with known genetic variants that are currently classified as having uncertain significance.
✕Less likely to help
Patients with genetic conditions that are already well understood and have established treatment protocols may not benefit from this research.
What this research studies
This research focuses on enhancing the prediction of how changes in amino acids within protein sequences impact their function. By utilizing advanced protein language models, the project aims to provide more accurate assessments of genetic variants, which are often classified as having unknown significance. The approach includes integrating various data sources, such as 3D structural information and clinical databases, to improve the understanding of these variants' effects. Patients may benefit from better insights into their genetic conditions and more informed clinical decisions.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnoses and personalized treatment options for patients with genetic variants.
How similar studies have gone: Previous research has shown promise in using protein language models for similar predictive tasks, indicating a potential for success in this novel application.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Ntranos, Vasileios — University of California, San Francisco
- Study coordinator: Ntranos, Vasileios
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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.