Evidence map›Paper›PMID 24854488›Full record

ReviewImmunology2014

A high-throughput shotgun mutagenesis approach to mapping B-cell antibody epitopes.

Edgar Davidson, Benjamin J Doranz

Abstract readReview
In one paragraph

Review in Immunology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed
–field-weighted citation impact
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

110 citing papers in PubMed.

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50 more citing papers are in PubMed but not listed here.

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

2 authors.

Edgar DavidsonIntegral Molecular Inc., Philadelphia, PA, USA.
Benjamin J Doranz

Funding

NIAID NIH HHS HHSN272200900055C
6 · The paper itself

Abstract

Characterizing the binding sites of monoclonal antibodies (mAbs) on protein targets, their 'epitopes', can aid in the discovery and development of new therapeutics, diagnostics and vaccines. However, the speed of epitope mapping techniques has not kept pace with the increasingly large numbers of mAbs being isolated. Obtaining detailed epitope maps for functionally relevant antibodies can be challenging, particularly for conformational epitopes on structurally complex proteins. To enable rapid epitope mapping, we developed a high-throughput strategy, shotgun mutagenesis, that enables the identification of both linear and conformational epitopes in a fraction of the time required by conventional approaches. Shotgun mutagenesis epitope mapping is based on large-scale mutagenesis and rapid cellular testing of natively folded proteins. Hundreds of mutant plasmids are individually cloned, arrayed in 384-well microplates, expressed within human cells, and tested for mAb reactivity. Residues are identified as a component of a mAb epitope if their mutation (e.g. to alanine) does not support candidate mAb binding but does support that of other conformational mAbs or allows full protein function. Shotgun mutagenesis is particularly suited for studying structurally complex proteins because targets are expressed in their native form directly within human cells. Shotgun mutagenesis has been used to delineate hundreds of epitopes on a variety of proteins, including G protein-coupled receptor and viral envelope proteins. The epitopes mapped on dengue virus prM/E represent one of the largest collections of epitope information for any viral protein, and results are being used to design better vaccines and drugs.

Indexed as

Antibodies, MonoclonalBinding Sites, AntibodyB-LymphocytesEpitope MappingHigh-Throughput Screening AssaysHumansMutagenesisAntibodies, MonoclonalenvelopeepitopeG protein-coupled receptorhigh-throughputmappingshotgun mutagenesis

Identifiers

PMID24854488
PMCPMC4137951

What OpenQuestion holds

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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.