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ARPA-H has awarded team SEEDS, co-led by the Buck Institute and Georgetown University, up to $51.7 million over four years to study how intrinsically disordered proteins shift toward disease-associated behavior. The consortium will combine AI-based predictions with experiments, initially focusing on Alzheimer’s disease and Ewing sarcoma. Whether the work will produce useful diagnostics or treatments remains unknown.
ARPA-H has awarded up to $51.7 million to a four-year research effort co-led by the Buck Institute for Research on Aging and Georgetown University to predict how flexible proteins begin to shift toward disease-associated behavior. The consortium, called team SEEDS, will pair computational models with laboratory experiments, initially studying Alzheimer’s disease and Ewing sarcoma.
SEEDS stands for Sequence-to-disease: Environment-aware Ensembles of Disordered proteins at Scale. The consortium brings together researchers from nine academic institutions and seven industry partners, combining expertise in protein science, artificial intelligence, computational modeling, cancer and neuroscience. Georgetown’s Priyanka Joshi, PhD, and the Buck Institute’s Chuankai (Kai) Zhou, PhD, co-lead the effort.
The team is studying intrinsically disordered proteins, which do not settle into one stable three-dimensional shape. Their behavior can change with conditions inside cells, including acidity, molecular crowding, metals, metabolites and chemical modifications. Such flexibility supports normal cellular functions, but some changes in these proteins are associated with disease.
SEEDS aims to connect a protein’s sequence and cellular surroundings to the range of shapes and behaviors it can take. Researchers plan to test computational predictions experimentally under different conditions. The source report says the project is intended to support new ways to detect, slow or treat disease; it does not report a diagnostic or treatment resulting from the work.
Aiming to Spot Protein Shifts Earlier
Many proteins linked to Alzheimer’s, amyotrophic lateral sclerosis and some cancers are difficult to target with conventional medicines. The source report says these proteins can lack the stable shapes and binding pockets that many drug designs rely on. Researchers can capture snapshots of some proteins, often after aggregation has occurred, but predicting how they behave as cellular conditions change remains a challenge.
If the consortium can reliably identify early changes associated with harmful behavior, that knowledge could inform research into earlier detection or ways to alter those changes. Those are project goals, not established outcomes. The award supports a research program; it does not show that a new test, preventive intervention or treatment is ready for patients.
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The BIOGAMI Research Program
SEEDS is part of ARPA-H’s BIOmolecular Grammar for protein Aggregation Modulation and Intervention program, known as BIOGAMI. The program focuses on early stages of protein dysfunction, before misfolding or aggregation causes lasting cellular damage. ARPA-H Program Manager Shannon Greene, PhD, leads BIOGAMI.
The program uses the idea of a molecular “grammar”: researchers seek rules that connect protein sequences and their surroundings to changes in behavior. AI systems such as AlphaFold have advanced protein science using extensive existing structural data. Zhou said researchers have less structural information for intrinsically disordered proteins, making them harder to model. SEEDS plans to generate data at scale while studying selected proteins in greater detail.
“We aim to uncover the principles that determine when these proteins remain in healthy functional ensembles and when they begin to shift toward disease-associated states.”
— Chuankai (Kai) Zhou, assistant professor at the Buck Institute
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What the Research Has Yet to Show
The announcement does not specify how much of the up to $51.7 million award will be paid, what milestones determine the final amount, or which proteins and experiments will be prioritized beyond the initial focus on Alzheimer’s disease and Ewing sarcoma. It also does not provide a detailed schedule for interim results.
It remains unknown whether the models will predict protein behavior accurately across different cellular conditions, or whether any predicted changes can be detected or altered in ways that affect disease. The project is a four-year research effort; the report describes intended work and potential applications, not clinical evidence or confirmed patient benefits.
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Four Years of Prediction and Testing
Over the planned four-year effort, SEEDS researchers intend to build models linking protein sequence and cellular environment to protein behavior, then compare those predictions with experimental measurements. The team will initially focus on Alzheimer’s disease and Ewing sarcoma as it develops the research platform.
The next meaningful evidence will come from the consortium’s data, experimental tests and progress against its research milestones. The source report gives no dates for publications, clinical studies or product development. Any future diagnostic or treatment would depend on results that have not yet been reported.
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Key Questions
Who is leading the SEEDS research effort?
The Buck Institute for Research on Aging and Georgetown University co-lead the consortium. Priyanka Joshi of Georgetown and Chuankai (Kai) Zhou of the Buck Institute are its co-leads.
How much funding did the team receive?
ARPA-H awarded the effort up to $51.7 million for a planned four-year project. The report does not state the amount that will ultimately be paid.
Which diseases will the researchers study first?
The initial focus is Alzheimer’s disease and Ewing sarcoma. The broader work concerns protein behavior associated with several diseases, including ALS and some cancers.
Will the project produce a treatment or diagnostic?
The team aims to develop knowledge that could support future diagnostics or treatments, but no resulting test or treatment is reported. The researchers plan to test model predictions experimentally during the project.
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