Mapping how proteins fold and misfold in natural and engineered proteins
Official title: High-throughput discovery of protein energy landscapes in natural and designed proteomes
Funded at Northwestern University · NIH-11515041 · Data updated 2026-11-12
The team is building a fast way to map hidden protein shapes to help people affected by protein-clumping diseases and those who use protein medicines.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Northwestern University NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project start | September 30, 2020 |
| Project end | May 31, 2025 |
| Funding year | 2026 |
| Award (this year) | $36,417 |
| NIH project number | 3DP2GM140927-01S1 |
| Project ID | NIH-11515041 on NIH RePORTER |
Who could benefit from this research
✓Could help
People with conditions linked to protein misfolding or aggregation (such as some neurodegenerative or organ-failure disorders) and patients who receive therapeutic protein drugs could be most relevant to the findings.
✕Less likely to help
Patients with conditions unrelated to protein folding, aggregation, or biologic therapies are unlikely to see direct benefits from this lab-focused work in the near term.
What this research studies
Researchers are developing a new high-speed lab test that uses hydrogen-exchange mass spectrometry to measure the hidden, higher-energy shapes of thousands of proteins at once. They will combine these large-scale measurements with machine learning to find patterns and with protein design to make safer molecules. The work looks at both natural human proteins and engineered therapeutic proteins to identify forms that can partially unfold, clump, or trigger immune reactions. This approach aims to make predictions much faster and cheaper than traditional experiments.
Why it matters
Potential benefit: If successful, this could lead to safer protein therapies, fewer immune reactions to biologic drugs, and better prevention or treatment strategies for diseases caused by protein aggregation.
How similar studies have gone: Detailed energy-landscape studies have succeeded for a small number of proteins, but combining massively parallel hydrogen-exchange measurements with machine learning and design at this scale is a novel approach.
Where this research is happening
-
Northwestern University
Chicago, United States Active
Researchers
- Principal investigator: Rocklin, Gabriel Jacob — Northwestern University
- Study coordinator: Rocklin, Gabriel Jacob
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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.