Predicting how genetic changes affect protein function using AI
Extending the utility and performance of variant effect predictors with protein language models
Using advanced protein-focused AI to give clearer, more reliable predictions about whether specific genetic changes may cause disease for people with genetic test results.
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 ID | NIH-11254907 on NIH RePORTER |
What this research studies
This project uses artificial intelligence models trained on protein sequences to better predict the effects of changes in your genes on protein function. Researchers will combine AI with 3D protein structure information, related gene data, and existing clinical databases to improve interpretation of genetic test results. They will also compare AI predictions to lab-based functional tests and look at whole-haplotype (combined variant) effects to reflect how real genetic backgrounds influence outcomes. The goal is to reduce the number of "variants of unknown significance" and make genetic findings more actionable for patients.
Who could benefit from this research
Good fit: People who have undergone genetic testing and received unclear or uncertain protein-coding variant results, or those with suspected inherited conditions tied to protein-altering variants, would be most relevant.
Not a fit: People whose conditions are not related to protein-altering genetic variants or who have not had genetic sequencing are unlikely to benefit directly from this project.
Why it matters
Potential benefit: If successful, this work could make genetic test reports more informative, reduce uncertain results, and help guide diagnosis and treatment decisions.
How similar studies have performed: Early studies show protein language models can improve variant prediction, but combining them with 3D structures, clinical data, and whole-haplotype analyses is a newer approach that still needs real-world validation.
Where this research is happening
San Francisco, United States
- University of California, San Francisco — San Francisco, United States (Active)
Researchers
- Principal investigator: Ntranos, Vasileios — University of California, San Francisco
- Study coordinator: Ntranos, Vasileios
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 below.