Evidence map›Paper›PMID 21439278›Full record

ArticleFEBS letters2011

Engineered epidermal growth factor mutants with faster binding on-rates correlate with enhanced receptor activation.

Jennifer L Lahti, Bertrand H Lui, Stayce E Beck, Stephen S Lee, Daphne P Ly, Michael T Longaker, George P Yang, Jennifer R Cochran

Open access · greenAbstract read
In one paragraph

Article in FEBS letters, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

9 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Growth Factor Engineering Strategies for Regenerative Medicine Applications.Frontiers in bioengineering and biotechnology · 2019
    Review
  6. Computational scheme for pH-dependent binding free energy calculation with explicit solvent.Protein science : a publication of the Protein Society · 2016
    Article
  7. Review
  8. Article
  9. 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

8 authors at 1 institution in 1 country.

Jennifer L LahtiDepartment of Bioengineering, Cancer Center, Bio-X Program, Stanford University, Stanford, CA 94305, USA.
Bertrand H Lui
Stayce E Beck
Stephen S Lee
Daphne P Ly
Michael T Longaker
George P Yang
Jennifer R Cochran
Stanford University · US

Funding

Regenerative Medicine Training Program (RMI)T90DK070103 · NIDDK · STANFORD UNIVERSITY · PI LONGAKER, MICHAEL T · 2004 to 2008
$1.8M
NIDDK NIH HHS T90 DK070103
6 · The paper itself

Abstract

Receptor tyrosine kinases (RTKs) regulate critical cell signaling pathways, yet the properties of their cognate ligands that influence receptor activation are not fully understood. There is great interest in parsing these complex ligand-receptor relationships using engineered proteins with altered binding properties. Here we focus on the interaction between two engineered epidermal growth factor (EGF) mutants and the EGF receptor (EGFR), a model member of the RTK superfamily. We found that EGF mutants with faster kinetic on-rates stimulate increased EGFR activation compared to wild-type EGF. These findings support previous predictions that faster association rates correlate with enhanced receptor activity.

Indexed as

MutationAmino Acid SequenceAnimalsBinding, CompetitiveCell LineCells, CulturedCHO CellsCricetinaeCricetulusEnzyme ActivationEpidermal Growth FactorErb-b2 Receptor Tyrosine KinasesErbB ReceptorsFibroblastsImmunoblottingKineticsEpidermal Growth FactorErb-b2 Receptor Tyrosine KinasesErbB ReceptorsMutant Proteins

Identifiers

PMID21439278
PMCPMC3118396
OpenAlexW2124234417

What OpenQuestion holds

Textmetadata
LicenceTDM
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.