A study published in Cell by researchers at NYU Langone Health, the Chinese Academy of Sciences, and Zhejiang University has identified a precision approach to treating autoimmune diseases — one that selectively silences harmful immune activity rather than broadly suppressing the immune system.
The research centers on a newly engineered bispecific antibody called BiTS — short for LAG-3/TCR Bispecific T cell Silencer. By simultaneously engaging the T cell receptor and LAG-3, an inhibitory protein found on T cells, BiTS is designed to locally “dial down” the overactive immune responses that drive autoimmune damage, without compromising the body’s broader defenses.
Tested across three mouse models, BiTS demonstrated meaningful results in each: reducing insulitis in a model of type 1 diabetes, limiting inflammatory liver damage in a hepatitis model, and alleviating multiple sclerosis”like symptoms. As the first therapeutic approach to exploit the precise spatial proximity between T cell receptors and LAG-3, the work opens a potential new class of treatments for a wide range of autoimmune conditions.
The study was supported in part by a translational advancement award from the Judith and Stewart Colton Center for Autoimmunity at NYU Langone — funding that directly enabled the development of the BiTS antibody and reflects the center’s focus on moving innovative immune therapies closer to clinical application.
Yale researchers have developed a machine learning model called ImmunoStruct that could significantly improve the design of personalized cancer vaccines — and the work was supported in part by the Colton Center for Autoimmunity at Yale. The study was published in Nature Machine Intelligence in February 2026.
The tool addresses a key limitation in existing vaccine design models: most treat peptides — the short protein fragments that the immune system recognizes as foreign — as flat, one-dimensional sequences of amino acids. ImmunoStruct instead incorporates three-dimensional structural and biochemical properties of peptides, giving the model a richer, more complete picture of how the immune system is likely to respond.
Epitope-based vaccines, which contain specific peptides designed to trigger targeted immune responses, are an emerging area of cancer immunotherapy showing promise for melanomas, breast cancers, and glioblastomas. By more accurately predicting which peptides will provoke the strongest immune response, ImmunoStruct could help researchers design vaccines tailored to a patient’s unique tumor profile — a potentially less toxic alternative to broad-spectrum therapies like chemotherapy.
The research was led by Professor Smita Krishnaswamy and Professor Akiko Iwasaki of Yale School of Medicine, with co-first authors Kevin B. Givechian and Chen Liu. The team has already licensed ImmunoStruct to Latent-Alpha, a Yale spinout company, and made the model available open source via GitHub.
The study is also notable as a direct output of the 2025 Colton Center at Yale grant cycle, in which ImmunoStruct was among the funded projects.
Featured Experts
Katsuo Kurabayashi, PhD
Colton Consortium Member
Department Chair, Mechanical and Aerospace Engineering, NYU Tandon School of Engineering
Carla R. Nowosad, PhD
Colton Consortium Member
Assistant Professor, Department of Pathology, NYU Grossman School of Medicine / NYU Langone Health
Jun Wang, PhD
Colton Consortium Member
Associate Professor, Department of Pathology, NYU Grossman School of Medicine / NYU Langone HealthFeatured Projects
Novel Tools to Track and Manipulate Immune Cells in Autoimmunity Models
Developing a cell-labeling tool to map immune cell interactions in living tissue, this project identifies the drivers of skin-resident T cell persistence in psoriasis and potential targets for disease prevention.
Early Detection and Diagnosis of Autoimmune Diseases Using Foundation AI Models
Applying self-supervised AI to multi-modal electronic health records — integrating clinical notes, labs, and imaging — this project builds scalable diagnostic models to detect autoimmune diseases earlier and more precisely.
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