How peptide sequences drive amyloid fiber formation
Elucidating the sequence code for amyloid peptide self-assembly through all-atom simulations, machine learning, and experiments
This project uses detailed computer simulations, machine learning, and lab tests to learn which short protein pieces tend to clump into amyloid fibers linked to Alzheimer's.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | New Jersey Institute of Technology NIH-funded |
| Lab location | 1 site (Newark, United States) |
| Project ID | NIH-11221679 on NIH RePORTER |
What this research studies
From a patient's perspective, researchers will combine atom-by-atom computer simulations, AI, and laboratory experiments to find the sequence patterns that make peptides form amyloid fibrils. They will run all-atom molecular dynamics to observe peptide assembly, train machine-learning models on those results, and validate key predictions with bench experiments. The aim is to create an accessible tool that predicts whether any peptide sequence will form fibrils and to distinguish disease-related amyloids from engineered, non-toxic ones. While this work is basic science, it supports future efforts to design safer biomaterials and to identify targets relevant to Alzheimer's disease.
Who could benefit from this research
Good fit: There is no patient enrollment expected; the findings are most relevant to people affected by Alzheimer's disease or other amyloid-related conditions who may benefit indirectly from future therapies informed by this work.
Not a fit: People with conditions unrelated to amyloid biology are unlikely to see direct benefit from this project.
Why it matters
Potential benefit: If successful, the project could help scientists identify or design peptides less likely to form toxic amyloid deposits, informing future treatments and safer biomaterials.
How similar studies have performed: Recent studies have shown that all-atom simulations can reproduce peptide fibril formation for some systems, but applying these methods broadly to predict any sequence remains a new and expanding effort.
Where this research is happening
Newark, United States
- New Jersey Institute of Technology — Newark, United States (Active)
Researchers
- Principal investigator: Dias, Cristiano Luis — New Jersey Institute of Technology
- Study coordinator: Dias, Cristiano Luis
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.