Evidence map›Paper›PMID 25536073›Full record

ArticleJournal of immunological methods2015

Measuring affinity constants of 1450 monoclonal antibodies to peptide targets with a microarray-based label-free assay platform.

J P Landry, Yaohuang Ke, Guo-Liang Yu, X D Zhu

Abstract read
In one paragraph

Article in Journal of immunological methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.

0numbers the graph read from it
0cells of the map it votes in
73citing 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

73 citing papers in PubMed.

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  8. Principles ofbioRxiv : the preprint server for biology · 2026
    Article
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  12. Label-Free Single-Molecule Immunoassay.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
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13 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

J P LandryDepartment of Physics, University of California, Davis, CA, 95616, USA.
Yaohuang KeEpitomics, Inc, Burlingame, CA 94010, USA.
Guo-Liang YuEpitomics, Inc, Burlingame, CA 94010, USA.
X D ZhuDepartment of Physics, University of California, Davis, CA, 95616, USA. Electronic address: xdzhu@physics.ucdavis.edu.

Funding

Oligosaccharides as antimicrobials and prebioticsR01HD065122 · NICHD · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CHEN, XI · 2010 to 2013
$1.8M
NICHD NIH HHS R01 HD065122NICHD NIH HHS R01-HD065122
6 · The paper itself

Abstract

Monoclonal antibodies (mAbs) are major reagents for research and clinical diagnosis. For their inherently high specificities to intended antigen targets and thus low toxicity in general, they are pursued as one of the major classes of new drugs. Yet binding properties of most monoclonal antibodies are not well characterized in terms of affinity constants and how they vary with presentations and/or conformational isomers of antigens, buffer compositions, and temperature. We here report a microarray-based label-free assay platform for high-throughput measurements of monoclonal antibody affinity constants to antigens immobilized on solid surfaces. Using this platform we measured affinity constants of over 1410 rabbit monoclonal antibodies and 46 mouse monoclonal antibodies to peptide targets that are immobilized through a terminal cysteine residue to a glass surface. The experimentally measured affinity constants vary from 10 pM to 200 pM with the median value at 66 pM. We compare the results obtained from the microarray-based platform with those from a benchmarking surface-plasmon-resonance-based (SPR) sensor (Biacore 3000).

Indexed as

AnimalsAntibodies, MonoclonalAntibody AffinityAntigen-Antibody ReactionsAntigensHigh-Throughput Screening AssaysMiceProtein Array AnalysisRabbitsSurface Plasmon ResonanceAntibodies, MonoclonalAntigensAffinity constantsLabel-free detectionMicroarrayMonoclonal antibodyOblique-incidence reflectivity differencePeptide

Identifiers

PMID25536073
PMCPMC4339518

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Registered trials

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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.