Evidence map›Paper›PMID 42482496›Full record

ArticleThe journal of physical chemistry letters2026

Protein-Protein Interactions Can Accelerate Transcription Factor Target Search on DNA.

Aniruddha Nagarajan, Sandeep Choubey, Anatoly B Kolomeisky

Abstract read
In one paragraph

Article in The journal of physical chemistry letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

3 authors.

Aniruddha NagarajanThe Institute of Mathematical Sciences , CIT Campus, Taramani, Chennai600113, India.
Sandeep ChoubeyThe Institute of Mathematical Sciences , CIT Campus, Taramani, Chennai600113, India.
Anatoly B KolomeiskyCenter for Theoretical Biological Physics, Rice University, Houston, Texas77005, United States.ORCID 0000-0001-5677-6690

Funding

Department of Biotechnology, Ministry of Science and Technology, India NADivision of Physics 2019745Welch Foundation C-1559
6 · The paper itself

Abstract

Protein molecules known as transcription factors (TFs) start gene expression by associating with specific promoter sites on DNA, initiating transcription. Recent studies have shown that various transcription factors interact with one another to form oligomers and phase-separated bodies. However, the impact of protein-protein interactions on the binding of TFs to their target sites on DNA remains elusive. To address this question, we developed a discrete-state stochastic model to quantitatively investigate the effect of dimerization of TFs in the target search on DNA. Using analytical calculations supported by kinetic Monte Carlo simulations, it is demonstrated that dimerization alters the effective association, dissociation, and sliding dynamics of TFs, leading to substantial changes in target search kinetics. Our results further show that the collective behavior of multiple TFs strongly depends on the dominant search regime. In particular, we identify parameter regimes in which dimerization significantly accelerates the localization of target sites, thereby enhancing the efficiency of the target search. We provide physical and chemical arguments to explain the origin of these effects and discuss recent experimental observations in light of our theoretical findings. Overall, the presented theoretical analysis indicates that tuning protein-protein interactions of TFs might be an efficient tool for the regulation of genetic information transfer.

Indexed as

DNATranscription FactorsDimerizationKineticsMonte Carlo MethodProtein BindingDNATranscription Factors

Identifiers

PMID42482496
PMCPMC13455104

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.