Evidence map›Paper›PMID 21825149›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2011

Phenotype-information-phenotype cycle for deconvolution of combinatorial antibody libraries selected against complex systems.

Hongkai Zhang, Ali Torkamani, Teresa M Jones, Diana I Ruiz, Jaume Pons, Richard A Lerner

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

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  17. Antibody selection using clonal cocultivation ofProceedings of the National Academy of Sciences of the United States of America · 2018
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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

6 authors.

Hongkai ZhangDepartment of Chemistry, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
Ali Torkamani
Teresa M Jones
Diana I Ruiz
Jaume Pons
Richard A Lerner

Funding

SCRIPPS TRANSLATIONAL SCIENCE INSTITUTE (UL1)UL1RR025774 · NCRR · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2008 to 2011
$16.3M
NCRR NIH HHS UL1 RR025774
6 · The paper itself

Abstract

Use of large combinatorial antibody libraries and next-generation sequencing of nucleic acids are two of the most powerful methods in modern molecular biology. The libraries are screened using the principles of evolutionary selection, albeit in real time, to enrich for members with a particular phenotype. This selective process necessarily results in the loss of information about less-fit molecules. On the other hand, sequencing of the library, by itself, gives information that is mostly unrelated to phenotype. If the two methods could be combined, the full potential of very large molecular libraries could be realized. Here we report the implementation of a phenotype-information-phenotype cycle that integrates information and gene recovery. After selection for phage-encoded antibodies that bind to targets expressed on the surface of Escherichia coli, the information content of the selected pool is obtained by pyrosequencing. Sequences that encode specific antibodies are identified by a bioinformatic analysis and recovered by a stringent affinity method that is uniquely suited for gene isolation from a highly degenerate collection of nucleic acids. This approach can be generalized for selection of antibodies against targets that are present as minor components of complex systems.

Indexed as

Peptide LibraryAntibodiesAntibodies, BacterialAntibody AffinityAntigens, BacterialCombinatorial Chemistry TechniquesDrug Evaluation, PreclinicalEscherichia coliPhenotypeSequence AnalysisAntibodiesAntibodies, BacterialAntigens, BacterialPeptide Library

Identifiers

PMID21825149
PMCPMC3158238

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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.