AI Meets Living Cells in $28.6 Million Bid to Predict Protein Misfolding Before Disease Strikes

Neurodegenerative diseases such as Alzheimer’s, Parkinson’s, ALS and Huntington’s share a sinister common thread: proteins that change shape, stick together and gradually damage the cells they once served. By the time these changes become visible under a microscope, much of the destruction may already be underway. A newly funded research effort now aims to catch the process at its very beginning, before the first molecular domino falls, and to train artificial intelligence to predict exactly when and how proteins are likely to go wrong. The project, backed by up to $28.6 million from the Advanced Research Projects Agency for Health, or ARPA-H, brings together a multi-institutional team of scientists who will combine large-scale experiments, human-cell studies and deep learning in a bid to decode one of biology’s most stubborn problems.

At the center of the experimental effort is Randal Halfmann, Ph.D., an Investigator at the Stowers Institute for Medical Research in Kansas City, whose laboratory has been selected to generate the project’s large-scale data on protein aggregation. His lab will receive approximately $4.1 million over two years as part of the initiative, which is called NATIVE-ID and is led by the Innovative Genomics Institute at the University of California, Berkeley. The work falls under ARPA-H’s BIOGAMI program, a broader effort to understand and ultimately control harmful protein aggregation, led by ARPA-H Program Manager Shannon Greene, Ph.D. Halfmann’s role is to supply the raw experimental fuel that the rest of the team will convert into predictive models.

The reason such a massive data-generating effort is needed lies in a fundamental blind spot in modern computational biology. Artificial intelligence has transformed scientists’ ability to predict the three-dimensional structures of many proteins, but roughly one-third of all proteins lack a stable structure altogether. These molecules, known as intrinsically disordered proteins, or IDPs, do not hold a single fixed shape. Instead, they shift among many conformations, and in doing so they can become entangled in harmful clumping, a process called aggregation that is closely associated with neurodegenerative disease. For AI systems trained largely on well-ordered structures, reading the behavior of these shape-shifting proteins remains an enormous challenge.

Halfmann believes the payoff for cracking that challenge could be transformative. Treatments for neurodegenerative diseases remain extremely limited, and most interventions arrive only after symptoms appear. If researchers could predict the probabilities and onset ages of disease from a person’s protein sequences, he argues, far more people could seek preventive or early-stage treatments or enroll in clinical trials before irreversible damage occurs. He has even proposed a more futuristic application: designing therapeutic IDPs that could intercept the problematic interactions driving diseases like Alzheimer’s, essentially fighting disordered proteins with engineered ones.

The key insight underlying the project is that even without a fixed structure, the behavior of an IDP is still governed by its amino-acid sequence. Small changes in that sequence can tip a protein between remaining soluble and beginning to clump. Halfmann’s lab will conduct large-scale experiments in yeast cells to test how sequence variations affect this balance, building on prior studies demonstrating that disease-relevant protein behavior observed in yeast can reliably inform how those same proteins behave in human cells. The workhorse technology is Distributed Amphifluoric FRET, or DAmFRET, a method his team developed in 2018 that measures protein self-assembly inside individual living cells.

The scale of the planned measurements is what sets this effort apart. Halfmann’s team will measure the aggregation tendencies of 50,000 proteins, each expressed individually in yeast cells under diverse conditions designed to mimic the cellular mishaps that accumulate as humans age. Across more than one million samples, the team expects to produce more than 10 billion individual measurements of protein aggregation. Direct measurements of protein aggregation at cellular resolution have not previously been done at anything approaching this scale, according to Halfmann, who noted that his laboratory has been laying the foundation for this moment since he joined Stowers in 2015.

That foundation includes a landmark achievement. In 2023, Halfmann’s lab became the first to experimentally determine the structure of the initiating step in amyloid formation associated with Huntington’s disease, work that involved the polyglutamine proteins implicated in that disorder. If scientists can figure out exactly how aggregation starts, Halfmann has argued, they can potentially stop the forest fire before it ignites. His lab has also studied TDP-43, a protein strongly associated with ALS and frontotemporal lobar degeneration. Those earlier studies, which examined hundreds of protein sequences, served as pilots for the new undertaking, which will scale the approach to 50,000 sequences.

The initial focus of NATIVE-ID is frontotemporal lobar degeneration, or FTLD, a neurodegenerative disease that shares significant genetic and biological features with ALS. The larger ambition, however, is to develop approaches that generalize across other diseases involving protein misfolding. The team’s other members bring complementary capabilities: generating human neurons from different genetic backgrounds, measuring protein interactions inside those neurons, developing advanced deep-learning frameworks, determining protein structures in test tubes, and running large-scale computational simulations of protein behavior. In addition to Halfmann, the collaboration includes scientists from UC Berkeley, Brown University, Emory University, Johns Hopkins University, Parallel Squared Technology Institute and Texas A&M University, with the Innovative Genomics Institute effort led by Executive Director Brad Ringeisen and Hanqin Li, head of the institute’s Advanced Translational Genetics lab.

Leadership at Stowers emphasizes that the experimental and computational halves of the project are inseparable. Alejandro Sánchez Alvarado, Ph.D., President and Chief Scientific Officer of the institute, noted that a deep learning model capable of decoding the language of disordered proteins needs rigorously obtained data at scale, something that has yet to exist. Halfmann’s laboratory, he said, will provide what has been missing: more than 10 billion measurements of aggregation, taken one living cell at a time, across 50,000 proteins, while colleagues at the Innovative Genomics Institute supply the human neurons in which the model’s predictions must ultimately hold true. Neither half of the project, he argued, can succeed without the other.

The award funds an initial two-year phase of work, structured within BIOGAMI as the first of two 24-month phases. Phase 1 is dedicated to delivering foundational datasets and models; Phase 2 will focus on validating potential therapeutics and on new ways to detect protein dysfunction earlier in the course of disease. For Halfmann, the timing is critical. Artificial intelligence now stands a real chance of decoding the language of intrinsically disordered proteins, he said, but only if it is provided sufficient high-quality data, and DAmFRET has finally matured enough to generate that data. If the collaboration succeeds, the earliest molecular whispers of Alzheimer’s, ALS and related diseases may one day be readable, and interceptable, years before the first symptom appears.

Subject of Research: AI prediction of intrinsically disordered protein aggregation in neurodegenerative disease

Article Title: Stowers scientist selected for $28.6 million effort to predict protein changes behind neurodegenerative disease

Article References: Stowers scientist selected for $28.6 million effort to predict protein changes behind neurodegenerative disease. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: protein aggregation, intrinsically disordered proteins, neurodegenerative disease, artificial intelligence, ARPA-H, DAmFRET, ALS, Huntington's disease, frontotemporal lobar degeneration, deep learning, Stowers Institute, protein misfolding