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Antigens Involved in Adaptive Immunity

Antigens Involved in Adaptive Immunity

An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
Complete Antigens
Complete antigens possess both immunogenicity and...

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Leveraging Artificial Intelligence for Neoantigen Prediction.

Jing Zeng¹, Zhengjun Lin¹, Xianghong Zhang¹

¹Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, People's Republic of China.

Cancer Research

|March 18, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) models are revolutionizing cancer immunotherapy by predicting tumor-specific neoantigens. These advanced AI tools enhance the discovery of immunogenic neoantigens for more effective cancer treatments.

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Last Updated: May 21, 2025

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

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Published on: November 17, 2018

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Area of Science:

Oncology
Immunology
Bioinformatics

Background:

Neoantigens, derived from tumor-specific mutations, are crucial targets for cancer immunotherapy.
Identifying immunogenic neoantigens that elicit anti-tumor immune responses is challenging due to complex interactions.
Experimental validation of neoantigens is resource-intensive and inefficient.

Purpose of the Study:

To provide a comprehensive summary of current artificial intelligence (AI) methodologies for neoantigen prediction.
To focus on AI's capability in modeling peptide-MHC (pMHC) and pMHC-TCR binding.
To benchmark the performance of antigen presentation predictors for neoantigen immunogenicity scoring.

Main Methods:

Review and summarization of existing AI methodologies for neoantigen prediction.
Analysis of AI models focusing on peptide-MHC (pMHC) binding prediction.
Benchmarking of AI-driven antigen presentation predictors for immunogenicity assessment.

Main Results:

AI models are increasingly utilized for discovering immunogenic neoantigens, overcoming experimental limitations.
The study assessed AI's effectiveness in modeling pMHC and pMHC-TCR interactions.
Benchmarking analysis evaluated the performance of AI predictors in scoring neoantigen immunogenicity.

Conclusions:

AI methodologies show significant potential for advancing tumor immunotherapy through precise neoantigen prediction.
Overcoming current limitations in data, algorithms, and validation is essential for clinical translation.
Future advancements in AI are expected to enhance the precision and utility of neoantigen discovery.