Traits of the deep studying mannequin and the coaching and analysis datasets for prediction of HLA-I epitopes. Credit score: Nature Machine Intelligence (2025). DOI: 10.1038/s42256-024-00971-y
A collaboration between the Ragon Institute and the Jameel Clinic at MIT has achieved a major milestone in leveraging synthetic intelligence (AI) to assist the event of T cell vaccine candidates.
Ragon school member Gaurav Gaiha, MD, DPhil, and MIT Professor Regina Barzilay, Ph.D., AI lead of the Jameel Clinic for AI and Well being, have revealed analysis in Nature Machine Intelligence introducing MUNIS—a deep studying software designed to foretell CD8+ T cell epitopes with unprecedented accuracy. This development has the potential to speed up vaccine improvement towards numerous infectious ailments.
The undertaking marks a significant first final result from the Mark and Lisa Schwartz AI/ML Initiative on the Ragon Institute, which goals to combine synthetic intelligence, machine studying, and translational immunology to stop and treatment infectious ailments of worldwide significance.
By combining the Gaiha Lab’s experience in T cell immunology with the Barzilay Lab’s pioneering work in AI, the crew—led by co-first authors Jeremy Wohlwend, Ph.D., and Anusha Nathan, Ph.D.—sought to deal with a longstanding problem in vaccine improvement: the speedy and correct identification of T cell epitopes in international pathogens. Epitopes are particular areas of an antigen which are acknowledged by the physique’s immune cells and are essential for activating focused immune responses.
Conventional strategies for predicting epitopes usually fall brief in velocity and accuracy. By integrating machine studying, researchers can now obtain quicker and extra environment friendly identification of T cell epitopes.
Utilizing a curated dataset of over 650,000 distinctive human leukocyte antigen (HLA) ligands and cutting-edge AI architectures, MUNIS considerably outperformed current epitope prediction fashions. The software was validated utilizing experimental information from influenza, HIV, and Epstein-Barr virus (EBV) and was capable of establish novel immunogenic epitopes in EBV, a virus that has been extensively studied.
Remarkably, MUNIS achieved accuracy corresponding to experimental stability assays, one other type of epitope prediction, demonstrating its potential to scale back laboratory burdens and streamline vaccine design.
“This is our first paper at the intersection of AI and immunology. Through this collaboration with Dr. Gaiha and his team, we learned a lot about this fascinating field and are excited about the immense possibilities in using AI algorithms to model the intricacies of the immune system,” Barzilay mentioned.
A key issue within the improvement of MUNIS was the collaboration between immunologists and pc scientists. The partnership leveraged the distinctive expertise and experience of every crew, making certain the software’s effectiveness in addressing organic complexities.
“This is a wonderful application of artificial intelligence that benefited greatly from insights shared by both computer scientists and immunologists,” Gaiha mentioned. “The credit lies with the initiative for bringing us together, which has led to the creation of an exciting new tool for immunology and vaccine design.”
The implications of this breakthrough lengthen past vaccine analysis. By offering a dependable technique to foretell which immunodominant epitopes are these most simply acknowledged by the immune system, MUNIS lays the muse for purposes in most cancers T cell immunotherapy and autoimmunity analysis. As the worldwide neighborhood continues to confront rising infectious ailments, instruments like MUNIS supply promise for enhanced preparedness.
This innovation underscores the Ragon Institute’s dedication to advancing science on the intersection of immunology and know-how to avoid wasting lives and promote world well being.
Extra info:
Jeremy Wohlwend et al, Deep studying enhances the prediction of HLA class I-presented CD8+ T cell epitopes in international pathogens, Nature Machine Intelligence (2025). DOI: 10.1038/s42256-024-00971-y
Supplied by
Ragon Institute of MGH, MIT and Harvard
Quotation:
AI development in T cell epitope prediction may propel vaccine improvement (2025, January 28)
retrieved 28 January 2025
from https://medicalxpress.com/information/2025-01-ai-advancement-cell-epitope-propel.html
This doc is topic to copyright. Other than any honest dealing for the aim of personal examine or analysis, no
half could also be reproduced with out the written permission. The content material is supplied for info functions solely.
