Evidence map›Paper›PMID 30386328›Full record

ReviewFrontiers in immunology2018

Understanding the Significance and Implications of Antibody Numbering and Antigen-Binding Surface/Residue Definition.

Mathieu Dondelinger, Patrice Filée, Eric Sauvage, Birgit Quinting, Serge Muyldermans, Moreno Galleni, Marylène S Vandevenne

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
6.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed, 108 citations in OpenAlex.

  1. Review
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  8. Review
  9. Article
  10. AntiBMPNN: Structure-Guided Graph Neural Networks for Precision Antibody Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  11. Article
  12. Article
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  15. Review
  16. Article
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  19. Computational and structural biotechnology journal · 2024
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Mathieu DondelingerCentre d'Ingénierie des Protéines, InBios, University of Liege, Liège, Belgium.
Patrice FiléeDépartement Biotechnologie, CER Groupe, Aye, Belgium.
Eric SauvageCentre d'Ingénierie des Protéines, InBios, University of Liege, Liège, Belgium.
Birgit QuintingCentre de Recherche des Instituts Groupés, Haute Ecole Libre Mosane, Liege, Belgium.
Serge MuyldermansDepartment of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium.
Moreno GalleniCentre d'Ingénierie des Protéines, InBios, University of Liege, Liège, Belgium.
Marylène S VandevenneCentre d'Ingénierie des Protéines, InBios, University of Liege, Liège, Belgium.
University of Liège · BECentre d’Economie rurale · BEHaute École Libre Mosane · BEVrije Universiteit Brussel · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monoclonal antibodies are playing an increasing role in both human and animal health. Different strategies of protein and chemical engineering, including humanization techniques of non-human antibodies were applied successfully to optimize clinical performances of antibodies. Despite the emergence of techniques allowing the development of fully human antibodies such as transgenic Xeno-mice, antibody humanization remains a standard procedure for therapeutic antibodies. An important prerequisite for antibody humanization requires standardized numbering methods to define precisely complementary determining regions (CDR), frameworks and residues from the light and heavy chains that affect the binding affinity and/or specificity of the antibody-antigen interaction. The recently generated deep-sequencing data and the increasing number of solved three-dimensional structures of antibodies from human and non-human origins have led to the emergence of numerous databases. However, these different databases use different numbering conventions and CDR definitions. In addition, the large fluctuation of the variable chain lengths, especially in CDR3 of heavy chains (CDRH3), hardly complicates the comparison and analysis of antibody sequences and the identification of the antigen binding residues. This review compares and discusses the different numbering schemes and "CDR" definition that were established up to date. Furthermore, it summarizes concepts and strategies used for numbering residues of antibodies and CDR residues identification. Finally, it discusses the importance of specific sets of residues in the binding affinity and/or specificity of immunoglobulins.

Indexed as

Amino AcidsAmino Acid SequenceAnimalsAntibodies, MonoclonalComplementarity Determining RegionsHumansImmunoglobulin Heavy ChainsImmunoglobulin Light ChainsMiceProtein DomainsAmino AcidsAntibodies, MonoclonalComplementarity Determining RegionsImmunoglobulin Heavy ChainsImmunoglobulin Light Chainsantibody engineeringantibody humanizationantigen binding residuecomplementary determining regionsnumbering scheme

Identifiers

PMID30386328
PMCPMC6198058
OpenAlexW2890382137

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.