Understanding how genetic variants affect protein function
Official title: High-throughput thermodynamic and kinetic measurements for variant effects prediction in a major protein superfamily
Funded at Stanford University · NIH-10910877 · Data updated 2026-11-12
This study is looking at how changes in our genes can affect the way proteins work in our bodies, and it’s designed to help doctors better understand how these genetic differences might influence our health, making personalized medicine more effective for everyone.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $42,407 |
| NIH project number | 5F31HG013267-02 |
| Project ID | NIH-10910877 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with known genetic variants that may affect protein function and are seeking insights into their health implications.
✕Less likely to help
Patients without any known genetic variants or those whose conditions are not related to protein function may not benefit from this research.
What this research studies
This research investigates how changes in the genetic code can alter the structure and function of proteins, which are crucial for many biological processes. By using advanced microfluidic techniques, the study aims to create large datasets that link specific genetic variations to their effects on protein behavior. This information will help improve predictive models that can assess the impact of genetic variants on health, ultimately enhancing the use of genomic data in personalized medicine.
Why it matters
Potential benefit: If successful, this research could lead to better predictions of how genetic variants influence disease, improving personalized treatment options for patients.
How similar studies have gone: Previous research has shown promise in using machine learning and biophysical data to predict protein function, indicating that this approach could yield significant advancements.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Olivas, Micah — Stanford University
- Study coordinator: Olivas, Micah
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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.