USF researchers develop AI framework to predict immune system responses
· News-MedicalA research team at the University of South Florida is taking a step in that direction by merging AI and immunology in ways that could enhance oncology treatment and the development of new drugs and vaccines.
In an embargoed new study publishing Wednesday, May 6, at 5 a.m. ET in Nature Machine Intelligence, researchers at the USF Health Morsani College of Medicine examined how well AI tools can predict one of the immune system's most important jobs: recognizing when something does not belong in the body.
That process is central to fighting infections and plays a major role in the development of immunotherapies, which are treatments designed to help a patient's own immune system attack disease.
The study was led by Xu and Fei He, assistant research professor also with the USF Health Informatics Institute. Xianyu Wang, an intern from the University of Missouri-Columbia, was also a co-author.
Their new framework can be applied to a broad class of immunology prediction problems, including peptide–HLA (human leukocyte antigen) binding; peptide–T-cell receptor interaction; antigen presentation; and other peptide- or antigen-driven interactions. These vital processes help immune cells identify what belongs in the body and what may be a threat.
"Our study tested how well AI tools can predict an important immune-system interaction that could help guide the development of cancer immunotherapies and vaccines,'' He said. "Our findings highlight the strengths and weaknesses of current AI approaches and provide guidance for building safe, more reliable AI tools for healthcare."
Immune cells recognize and react to antigens, which are proteins on bacteria, viruses or tumor cells that can act as foreign markers, alerting the immune system to a possible threat.
Adaptive immune cells, including T and B cells, use specific receptors to recognize harmful invaders such as viruses, allergens, toxins or cancer cells. Other immune cells ingest these invaders, break them into pieces of antigens and present those pieces to activate a targeted immune defense.
The team used PanPep and other tools to predict how T-cell receptors behave in binding to antigens. Developed to address the challenges of limited data the tool can create scenarios to predict binding for unseen or rare peptides, which are small chains of amino acids that can serve as key immune-system targets.
Accurately predicting peptide and T-cell receptor binding allows scientists to identify and design the right "trigger" peptides for specific immune cells. Those trigger peptides could accelerate immunotherapies and save lives.
By narrowing down the best candidates for laboratory testing, researchers can reduce the need for large-scale biological experiments that are time-consuming and costly.
The USF research represents a significant step toward more reliable AI-guided, personalized cancer therapies and vaccines. For example, with tools such as PanPep, scientists may be able to simulate oncology screening processes on computers, potentially reducing time frames from months or years to a matter of days.
If doctors can quickly identify a promising treatment for a person with stage-4 cancer, for instance, it could extend their life. But the authors note that while meta-learning approaches can build accurate, target-specific models using only a small amount of experimental data, they require careful testing and refinement before they can be safely used to guide personalized care.
"Since real-world applications often involve entirely new immune targets, it remains unclear to what extent these models can handle truly unseen cases," the authors said. "This is the initial rationale of this study."
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