Evidence map›Paper›PMID 28774855›Full record

ArticleBiochimica et biophysica acta. General subjects2017

Using competition assays to quantitatively model cooperative binding by transcription factors and other ligands.

Jacob Peacock, James B Jaynes

Abstract read
In one paragraph

Article in Biochimica et biophysica acta. General subjects, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jacob PeacockDept. of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA 19107, United States.
James B JaynesDept. of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA 19107, United States. Electronic address: james.jaynes@jefferson.edu.

Funding

TRANSCRIPTIONAL REGULATORY MECHANISMS DURING DEVELOPMENTR01GM050231 · NIGMS · THOMAS JEFFERSON UNIVERSITY · PI JAYNES, JAMES B · 1995 to 2013
$3.3M
Insulators: Determinants of chromosome topology and regulatory interactionsR01GM117458 · NIGMS · THOMAS JEFFERSON UNIVERSITY · PI JAYNES, JAMES B · 2016 to 2019
$1.2M
NIGMS NIH HHS R01 GM050231NIGMS NIH HHS R01 GM117458
6 · The paper itself

Abstract

backgroundThe affinities of DNA binding proteins for target sites can be used to model the regulation of gene expression. These proteins can bind to DNA cooperatively, strongly impacting their affinity and specificity. However, current methods for measuring cooperativity do not provide the means to accurately predict binding behavior over a wide range of concentrations.

methodsWe use standard computational and mathematical methods, and develop novel methods as described in Results.

resultsWe explore some complexities of cooperative binding, and develop an improved method for relating in vitro measurements to in vivo function, based on ternary complex formation. We derive expressions for the equilibria among the various complexes, and explore the limitations of binding experiments that model the system using a single parameter. We describe how to use single-ligand binding and ternary complex formation in tandem to determine parameters that have thermodynamic relevance. We develop an improved method for finding both single-ligand dissociation constants and concentrations simultaneously. We show how the cooperativity factor can be found when only one of the single-ligand dissociation constants can be measured.

conclusionsThe methods that we develop constitute an optimized approach to accurately model cooperative binding. GENERAL SIGNIFICANCE: The expressions and methods we develop for modeling and analyzing DNA binding and cooperativity are applicable to most cases where multiple ligands bind to distinct sites on a common substrate. The parameters determined using these methods can be fed into models of higher-order cooperativity to increase their predictive power.

Indexed as

ThermodynamicsAllosteric RegulationBinding SitesBiophysical PhenomenaComputational BiologyDNA-Binding ProteinsKineticsLigandsModels, TheoreticalProtein BindingTranscription FactorsDNA-Binding ProteinsLigandsTranscription FactorsCompetition EMSACooperative DNA bindingCurve fittingEngrailedExtradenticle-HomothoraxFinding dissociation constantsHill plotsModeling ligand-substrate interactionsQuantifying cooperativity

Identifiers

PMID28774855
PMCPMC5623634

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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.