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Related Concept Videos

Antibody Structure01:10

Antibody Structure

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Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
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In 1971, Peter Perlman and Eva Engvall developed an Enzyme-linked immunosorbent assay (ELISA or EIA). ELISA differs from western blot in that the assays are conducted in microtiter plates or in vivo rather than on an absorbent membrane.
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Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
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Simple proteins and protein complexes contain only amino acids. In contrast, many other proteins, called conjugated proteins, covalently bond with non-protein moieties.
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Updated: Jan 18, 2026

Detection of SARS-CoV-2 Receptor-Binding Domain Antibody using a HiBiT-Based Bioreporter
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CoV-UniBind: a unified antibody binding database for SARS-CoV-2.

Aryan Bhasin1, Francesco Saccon1, Callum Canavan1

  • 1Applied AI, InstaDeep Ltd, London, W2 1AY, United Kingdom.

Bioinformatics Advances
|January 16, 2026
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Summary

We created CoV-UniBind, a comprehensive database of SARS-CoV-2 antibody-antigen interactions, to aid machine learning in antibody design. This unified resource supports vaccine development against SARS-CoV-2 variants.

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Area of Science:

  • Immunology
  • Structural Biology
  • Bioinformatics

Background:

  • Numerous studies investigated SARS-CoV-2 antibody interactions with viral variants.
  • Existing antibody-antigen data are fragmented, hindering machine learning model development.
  • There is a need for a unified, standardized dataset for computational antibody research.

Purpose of the Study:

  • To present CoV-UniBind, a unified database of SARS-CoV-2 antibody-antigen data.
  • To facilitate the development and validation of machine learning models for antibody-antigen interaction prediction.
  • To support antibody and vaccine development against SARS-CoV-2.

Main Methods:

  • Integrated and standardized data from three public sources and peer-reviewed publications.
  • Compiled over 75,000 entries of antibody-antigen sequence, binding, and structural data for SARS-CoV-2.
  • Benchmarked protein folding, inverse folding, and language models using the CoV-UniBind database.

Main Results:

  • CoV-UniBind provides a unified resource of SARS-CoV-2 antibody-antigen data.
  • The database facilitates benchmarking of computational models for antibody design.
  • Demonstrated the utility of the database for tasks relevant to vaccine development.

Conclusions:

  • CoV-UniBind is a valuable resource for computational antibody research.
  • The database is expected to accelerate the development of antibodies and vaccines against SARS-CoV-2.
  • Standardized data integration is crucial for advancing machine learning applications in immunology.