ArticleAccounts of chemical research2016
Elucidation of the Dynamics of Transcription Elongation by RNA Polymerase II using Kinetic Network Models.
Article in Accounts of chemical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 66 citations in OpenAlex.
- Article
- Applying the brakes to transcription: regulation of gene expression by RNA polymerase pausing.Journal of bacteriology · 2025Review
- Kinetic mechanisms for the sequence dependence of transcriptional errors.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Memory kernel minimization-based neural networks for discovering slow collective variables of biomolecular dynamics.Nature computational science · 2025Article
- AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.Journal of chemical theory and computation · 2025Article
- Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.Biochemistry · 2025Review
- RNA Polymerase II Activity Control of Gene Expression and Involvement in Disease.Journal of molecular biology · 2025Review
- Structural basis of transcription: RNA polymerase II substrate binding and metal coordination using a free-electron laser.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.The Journal of chemical physics · 2024Article
- Quantifying the impact of initial RNA primer length on nucleotide addition by RNA polymerase I.Biophysical chemistry · 2024Article
- GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics.The Journal of chemical physics · 2023Article
- Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.PLoS computational biology · 2023Article
- Transient-State Kinetic Analysis of the RNA Polymerase II Nucleotide Incorporation Mechanism.Biochemistry · 2023Article
- Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning.JACS Au · 2021Review
- Role of bacterial RNA polymerase gate opening dynamics in DNA loading and antibiotics inhibition elucidated by quasi-Markov State Model.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Super enhancers-Functional cores under the 3D genome.Cell proliferation · 2021Review
- Nucleotide Loading Modes of Human RNA Polymerase II as Deciphered by Molecular Simulations.Biomolecules · 2020Article
- Ligand-bound glutamine binding protein assumes multiple metastable binding sites with different binding affinities.Communications biology · 2020Article
- The Mechanisms of Substrate Selection, Catalysis, and Translocation by the Elongating RNA Polymerase.Journal of molecular biology · 2019Review
- 8-Oxo-guanine DNA damage induces transcription errors by escaping two distinct fidelity control checkpoints of RNA polymerase II.The Journal of biological chemistry · 2019Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
RNA polymerase II (Pol II) is an essential enzyme that catalyzes transcription with high efficiency and fidelity in eukaryotic cells. During transcription elongation, Pol II catalyzes the nucleotide addition cycle (NAC) to synthesize mRNA using DNA as the template. The transitions between the states of the NAC require conformational changes of both the protein and nucleotides. Although X-ray structures are available for most of these states, the dynamics of the transitions between states are largely unknown. Molecular dynamics (MD) simulations can predict structure-based molecular details and shed light on the mechanisms of these dynamic transitions. However, the employment of MD simulations on a macromolecule (tens to hundreds of nanoseconds) such as Pol II is challenging due to the difficulty of reaching biologically relevant timescales (tens of microseconds or even longer). For this challenge to be overcome, kinetic network models (KNMs), such as Markov State Models (MSMs), have become a popular approach to access long-timescale conformational changes using many short MD simulations. We describe here our application of KNMs to characterize the molecular mechanisms of the NAC of Pol II. First, we introduce the general background of MSMs and further explain procedures for the construction and validation of MSMs by providing some technical details. Next, we review our previous studies in which we applied MSMs to investigate the individual steps of the NAC, including translocation and pyrophosphate ion release. In particular, we describe in detail how we prepared the initial conformations of Pol II elongation complex, performed MD simulations, extracted MD conformations to construct MSMs, and further validated them. We also summarize our major findings on molecular mechanisms of Pol II elongation based on these MSMs. In addition, we have included discussions regarding various key points and challenges for applications of MSMs to systems as large as the Pol II elongation complex. Finally, to study the overall NAC, we combine the individual steps of the NAC into a five-state KNM based on a nonbranched Brownian ratchet scheme to explain the single-molecule optical tweezers experimental data. The studies complement experimental observations and provide molecular mechanisms for the transcription elongation cycle. In the long term, incorporation of sequence-dependent kinetic parameters into KNMs has great potential for identifying error-prone sequences and predicting transcription dynamics in genome-wide transcriptomes.
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