Evidence map›Paper›PMID 26060075›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2015

Identification of Novel Protein-Ligand Interactions by Exon Microarray Analysis of Yeast Surface Displayed cDNA Library Selection Outputs.

Scott Bidlingmaier, Bin Liu

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Scott BidlingmaierDepartment of Anesthesia, UCSF Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, 1001 Potrero Avenue, 1305, San Francisco, CA, 94110, USA.
Bin Liu

Funding

Mapping a clinically significant internalizing tumor epitope space.R01CA118919 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LIU, BIN · 2006 to 2013
$2.5M
Internalizing human antibody-targeted nanosized siRNA therapeuticsR01CA171315 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LIU, BIN · 2012 to 2016
$1.6M
Identifying antigens bound by novel scFvs targeting all subtypes of mesotheliomaR01CA129491 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LIU, BIN · 2010 to 2013
$1.2M
NCI NIH HHS R01 CA118919NCI NIH HHS R01 CA129491NCI NIH HHS R01 CA171315
6 · The paper itself

Abstract

Yeast surface display is widely utilized to screen large libraries for proteins or protein fragments with specific binding properties. We have previously constructed and utilized yeast surface displayed human cDNA libraries to identify protein fragments that bind to various target ligands. Conventional approaches employ monoclonal screening and sequencing of polyclonal outputs that have been enriched for binding to a target molecule by several rounds of affinity-based selection. Frequently, a small number of clones will dominate the selection output, making it difficult to comprehensively identify potentially important interactions due to low representation in the selection output. We have developed a novel method to address this problem. By analyzing selection outputs using high-density human exon microarrays, the full potential of selection output diversity can be revealed in one experiment. FACS-based selection using yeast surface displayed human cDNA libraries combined with exon microarray analysis of the selection outputs is a powerful way of rapidly identifying protein fragments with affinity for any soluble ligand that can be fluorescently detected, including small biological molecules and drugs. In this report we present protocols for exon microarray-based analysis of yeast surface display human cDNA library selection outputs.

Indexed as

ExonsCloning, MolecularGene LibraryHumansOligonucleotide Array Sequence AnalysisProtein BindingProteinsSaccharomyces cerevisiaeProteins

Identifiers

PMID26060075
PMCPMC4842228

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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.