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Hybridoma Technology

Hybridoma Technology

Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...

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Updated: Oct 16, 2025

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

epitope3D: a machine learning method for conformational B-cell epitope prediction.

Bruna Moreira da Silva^1,2,3,4, YooChan Myung^1,2,3,5, David B Ascher^1,2,3,5,6

¹Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.

Briefings in Bioinformatics

|October 22, 2021

Summary

This summary is machine-generated.

Identifying conformational epitopes is crucial for vaccine development. A new machine learning method, epitope3D, accurately predicts these epitopes using graph-based signatures, outperforming existing tools.

Keywords:

conformational epitope graph-based signatures machine learning

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Last Updated: Oct 16, 2025

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

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Published on: March 24, 2017

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

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Published on: March 25, 2014

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

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Published on: March 15, 2019

Area of Science:

Immunology
Computational Biology
Machine Learning

Background:

Accurate identification of antigenic determinants (epitopes) is vital for vaccine design and immunotherapy, especially during pandemics.
Existing computational tools for epitope prediction show limited performance, particularly for conformational B-cell epitopes.
There is a need for improved methods to accurately predict conformational epitopes.

Purpose of the Study:

To introduce epitope3D, a novel scalable machine learning method for accurate conformational epitope identification.
To evaluate epitope3D's performance using the largest curated epitope dataset available.
To compare epitope3D against existing computational approaches.

Main Methods:

Developed epitope3D, a machine learning method utilizing graph-based signatures.
Modeled epitope and non-epitope regions as graphs to extract distance patterns.
Trained and tested predictive models on a large, curated epitope dataset.

Main Results:

epitope3D demonstrated superior performance compared to alternative methods.
Achieved a Mathew's Correlation Coefficient of 0.55 and F1-score of 0.57 on cross-validation.
Attained a Mathew's Correlation Coefficient of 0.45 and F1-score of 0.36 on independent blind tests.

Conclusions:

epitope3D represents a significant advancement in conformational epitope prediction.
The method's accuracy and scalability offer potential for improved vaccine development and pandemic response.
Graph-based signatures provide an effective approach for modeling epitope regions.