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Related Concept Videos

Antibody Actions01:26

Antibody Actions

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Antibodies, or immunoglobulins, are critical players in the immune system's arsenal against invading pathogens. Produced by B cells and plasma cells, their primary role is to detect and bind to specific antigens, molecules found on the surface of pathogens like bacteria or viruses. Beyond antigen recognition, antibodies perform several vital functions that contribute to immune defense.
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Immunoprecipitation01:20

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Immunoprecipitation, or IP, is a widely used technique that employs protein-antibody interactions to isolate proteins or protein complexes in their native state for studying protein-protein interactions, quaternary structures, or supramolecular complexes. Various modifications of the technique, including chromatin IP, cross-linking IP, and fluorescence IP, are commonly used.
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Updated: Aug 14, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
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AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.

Yan Huang1,2, Ziding Zhang1, Yuan Zhou2

  • 1State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.

Frontiers in Immunology
|January 9, 2023
PubMed
Summary
This summary is machine-generated.

A new deep learning method, AbAgIntPre, rapidly predicts antibody-antigen interactions using only amino acid sequences, accelerating antibody design and reducing experimental costs.

Keywords:
SARS-CoVSiamese-like convolutional neural networkantibody-antigen interactiondeep learningsequence featurewebserver

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • Antibody-mediated immunity is crucial for vertebrate defense.
  • Antibodies are vital in treating diseases like cancer, but identifying antibody-antigen interactions is challenging.
  • Current computational methods for antibody screening often require 3D structures, limiting their applicability.

Purpose of the Study:

  • To develop a rapid and accurate computational method for predicting antibody-antigen interactions.
  • To overcome the limitations of existing methods by relying solely on amino acid sequences.

Main Methods:

  • Developed AbAgIntPre, a deep learning model utilizing a Siamese-like convolutional neural network architecture.
  • Employed an amino acid composition encoding scheme for both antibody and antigen sequences.
  • The method requires only the amino acid sequences for prediction.

Main Results:

  • The generic AbAgIntPre model achieved an Area Under Curve (AUC) of 0.82 on an independent test dataset.
  • Demonstrated competitive performance on a specific SARS-CoV dataset.
  • The method provides a fast and sequence-based approach to predict antibody-antigen interactions.

Conclusions:

  • AbAgIntPre offers a significant advancement in predicting antibody-antigen interactions.
  • This deep learning approach complements traditional experimental methods, reducing workload in antibody design.
  • A freely accessible web server is available for AbAgIntPre.