ArticleThe journal of physical chemistry. B2017
Deficiencies in Molecular Dynamics Simulation-Based Prediction of Protein-DNA Binding Free Energy Landscapes.
Article in The journal of physical chemistry. B, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed.
- The Screening Strategy and Activity Investigation of Skipjack Tuna (Foods (Basel, Switzerland) · 2025Article
- Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction.Journal of chemical theory and computation · 2025Article
- DNA methylation of the promoter region at the CREB1 binding site is a mechanism for the epigenetic regulation of brain-specific PKMζ.Biochimica et biophysica acta. Gene regulatory mechanisms · 2023Article
- Structural predictions of protein-DNA binding: MELD-DNA.Nucleic acids research · 2023Article
- Searching for Low Probability Opening Events in a DNA Sliding Clamp.Life (Basel, Switzerland) · 2022Article
- The Impact of the HydroxyMethylCytosine epigenetic signature on DNA structure and function.PLoS computational biology · 2021Article
- Revealing atomic-scale molecular diffusion of a plant-transcription factor WRKY domain protein along DNA.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field.Annual review of biophysics · 2021Article
- Interaction of camel Lactoferrin derived peptides with DNA: a molecular dynamics study.BMC genomics · 2020Article
- DNA-protein interaction: identification, prediction and data analysis.Molecular biology reports · 2019Review
- RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview.Chemical reviews · 2018Review
Corrections and comments
- Expression of concern · 2018-05-09Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions ·
- Erratum issued
- Expression of concern
Authors and funding
2 authors.
Funding
Abstract
Transcriptional regulation allows cells to match their gene expression profiles to their current requirements based on environment, cellular physiological state, and extracellular signals. DNA binding transcription factors are major agents of transcriptional regulation, and bind to DNA with a factor-specific sequence preference to exert regulatory effects. A crucial step in unraveling the logic of a regulatory network is determining the sequence-specific binding affinity landscapes for the transcription factors in it. While such landscapes can be measured experimentally, the ability to predict them computationally would both reduce the effort required to obtain the needed data and provide additional insight into the key interactions shaping protein-DNA interactions. Here we apply free energy calculations based on all-atom molecular dynamics simulations to predict the changes in binding free energy for all single base pair perturbations of the binding sites for four eukaryotic transcription factors for which high-quality experimental data exist. We find that the simulated results both vastly overestimate the magnitude of changes in binding free energy, and frequently predict the incorrect signs. These simulations will nevertheless serve as a jumping-off point for refining our current representation of protein-DNA interactions to allow quantitative reproduction of experimental data on such systems in the future.
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Registered trials
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.