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Updated: Sep 4, 2025

Detection of Neutralization-sensitive Epitopes in Antigens Displayed on Virus-Like Particle VLP-Based Vaccines Using a Capture Assay

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A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods.

Rui Yin¹, Xianghe Zhu², Min Zeng³

¹Department of Biomedical Informatics, Harvard Medical School, Boston, USA.

Briefings in Bioinformatics

|July 18, 2022

Summary

This summary is machine-generated.

This study introduces a new machine learning framework to accurately predict linear B-cell epitopes in viruses. This advancement aids in developing vaccines and diagnostics for infectious diseases.

Keywords:

Epitope prediction Human viruses Machine learning Variable-length

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Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

Area of Science:

Virology
Immunology
Bioinformatics
Machine Learning

Background:

The COVID-19 pandemic highlighted the threat of viruses and the importance of vaccines.
Identifying B-cell epitopes is crucial for vaccine design, diagnostics, and antibody production.
Existing experimental and in silico methods for epitope prediction are often time-consuming, expensive, or lack satisfactory performance.

Purpose of the Study:

To develop a general machine learning framework for predicting linear B-cell epitopes specific to human-adapted viruses.
To improve the accuracy and efficiency of B-cell epitope identification compared to existing methods.

Main Methods:

Utilized machine learning approaches based on Protvec peptide representation and amino acid physicochemical properties.
Incorporated QR decomposition to enable models to handle variable-length peptide sequences.
Trained and validated the framework on large immune epitope datasets.

Main Results:

The proposed model demonstrated superior performance over state-of-the-art methods, achieving an AUROC of 0.827 and AUPR of 0.831 on the testing set.
Achieved high precision in viral category classification for predicted epitopes through sequence analysis.
The framework reliably identifies linear B-cell epitopes from protein sequences of human-adapted viruses.

Conclusions:

The developed framework offers a reliable and efficient method for identifying linear B-cell epitopes in human-adapted viruses.
This tool can significantly assist in vaccine design, disease diagnostics, and antibody production, particularly in response to future pandemics and epidemics.