Mapping how proteins fold and misfold in natural and engineered proteins

High-throughput discovery of protein energy landscapes in natural and designed proteomes

NIH-funded research Northwestern University · NIH-11515041

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 typeNIH-funded research
Study typeNIH-funded research
Funding institutionNorthwestern University NIH-funded
Lab location1 site (Chicago, United States)
Project IDNIH-11515041 on NIH RePORTER

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.

Who could benefit from this research

Good fit: 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.

Not a fit: 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.

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 performed: 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

Chicago, United States

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Last reviewed 2026-06-13 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.