scholarly journals RNA polymerase II conserved protein domains as platforms for protein-protein interactions

Transcription ◽  
2011 ◽  
Vol 2 (4) ◽  
pp. 193-197 ◽  
Author(s):  
M. Carmen García-López ◽  
Francisco Navarro
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Protein Interactions ◽  
Protein Domains ◽  
Protein Protein Interactions

scholarly journals Evidence that Negative Elongation Factor Represses Transcription Elongation through Binding to a DRB Sensitivity-Inducing Factor/RNA Polymerase II Complex and RNA

2002 ◽  
Vol 22 (9) ◽  
pp. 2918-2927 ◽  
Author(s):  
Yuki Yamaguchi ◽  
Naoto Inukai ◽  
Takashi Narita ◽  
Tadashi Wada ◽  
Hiroshi Handa
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Protein Interactions ◽  
Rna Binding ◽  
Elongation Factor ◽  
Protein Protein Interactions ◽  
Negative Elongation Factor ◽  
Nascent Transcripts

ABSTRACT Negative elongation factor (NELF) is a human transcription factor complex that cooperates with DRB sensitivity-inducing factor (DSIF)/hSpt4-hSpt5 to repress elongation by RNA polymerase II (RNAPII). NELF activity is associated with five polypeptides, including NELF-A, a candidate gene product for Wolf-Hirschhorn syndrome, and NELF-E, a putative RNA-binding protein with arginine-aspartic acid (RD) dipeptide repeats. Here we report several important findings regarding the DSIF/NELF-dependent elongation control. First, we have established an effective method for purifying the active NELF complex using an epitope-tagging technique. Second, the five polypeptides each are important and together are sufficient for its function in vitro. Third, NELF does not bind to either DSIF or RNAPII alone but does bind to the preformed DSIF/RNAPII complex. Fourth, NELF-E has a functional RNA-binding domain, whose mutations impair transcription repression without affecting known protein-protein interactions. Taken together, we propose that NELF causes RNAPII pausing through binding to the DSIF/RNAPII complex and to nascent transcripts. These results also have implications for how DSIF and NELF are regulated in a gene-specific manner in vivo.

scholarly journals Transient DNA Binding Induces RNA Polymerase II Compartmentalization During Herpesviral Infection Distinct From Phase Separation

2018 ◽  
Author(s):  
David T McSwiggen ◽  
Anders S Hansen ◽  
Hervé Marie-Nelly ◽  
Sheila Teves ◽  
Alec B Heckert ◽  
...  
Keyword(s):  
Phase Separation ◽  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Protein Interactions ◽  
Viral Dna ◽  
Herpes Simplex Virus 1 ◽  
Pol Ii ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Simplex Virus

SummaryDuring lytic infection, Herpes Simplex Virus 1 generates replication compartments (RCs) in host nuclei that efficiently recruit protein factors, including host RNA Polymerase II (Pol II). Pol II and other cellular factors form hubs in uninfected cells that are proposed to phase separate via multivalent protein-protein interactions mediated by their intrinsically disordered regions. Using a battery of live cell microscopic techniques, we show that although RCs superficially exhibit many characteristics of phase separation, the recruitment of Pol II instead derives from nonspecific interactions with the viral DNA. We find that the viral genome remains nucleosome-free, profoundly affecting the way Pol II explores RCs by causing it to repetitively visit nearby binding sites, thereby creating local Pol II accumulations. This mechanism, distinct from phase separation, allows viral DNA to outcompete host DNA for cellular proteins. Our work provides new insights into the strategies used to create local molecular hubs in cells.

scholarly journals A Minimal Set of RNA Polymerase II Transcription Protein Interactions

1996 ◽  
Vol 271 (33) ◽  
pp. 20170-20174 ◽  
Author(s):  
David A. Bushnell ◽  
Cynthia Bamdad ◽  
Roger D. Kornberg
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Protein Interactions ◽  
Minimal Set ◽  
Rna Polymerase Ii Transcription

scholarly journals Two- Dimensional Macroscopic Protein Domains Induced by the Interplay between Lipid- Protein and Protein- Protein Interactions

2013 ◽  
Vol 104 (2) ◽  
pp. 248a-249a
Author(s):  
Wan-Ting Hsieh ◽  
Zhengzheng Liao ◽  
Chih-Jung Hsu ◽  
Ivan J. Dmochowski ◽  
Tobias Baumgart
Keyword(s):  
Protein Interactions ◽  
Protein Domains ◽  
Two Dimensional ◽  
Protein Protein Interactions

scholarly journals RNA Polymerase Tags To Monitor Multidimensional Protein–Protein Interactions Reveal Pharmacological Engagement of Bcl-2 Proteins

2017 ◽  
Vol 139 (34) ◽  
pp. 11964-11972 ◽  
Author(s):  
Jinyue Pu ◽  
Jeffrey A. Dewey ◽  
Abbas Hadji ◽  
James L. LaBelle ◽  
Bryan C. Dickinson
Keyword(s):  
Rna Polymerase ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Critical Protein–Protein Interactions Determine the Biological Activity of Elk-1, A Master Regulator of Stimulus-Induced Gene Transcription

Molecules ◽  
2021 ◽  
Vol 26 (20) ◽  
pp. 6125
Author(s):  
Gerald Thiel ◽  
Tobias M. Backes ◽  
Lisbeth A. Guethlein ◽  
Oliver G. Rössler
Keyword(s):  
Protein Kinase ◽  
Rna Polymerase Ii ◽  
Histone Deacetylase ◽  
Gene Transcription ◽  
Protein Interactions ◽  
Cellular Proliferation ◽  
Biological Function ◽  
Protein Protein Interactions ◽  
Inactive State ◽  
Serum Response

Elk-1 is a transcription factor that binds together with a dimer of the serum response factor (SRF) to the serum-response element (SRE), a genetic element that connects cellular stimulation with gene transcription. Elk-1 plays an important role in the regulation of cellular proliferation and apoptosis, thymocyte development, glucose homeostasis and brain function. The biological function of Elk-1 relies essentially on the interaction with other proteins. Elk-1 binds to SRF and generates a functional ternary complex that is required to activate SRE-mediated gene transcription. Elk-1 is kept in an inactive state under basal conditions via binding of a SUMO-histone deacetylase complex. Phosphorylation by extracellular signal-regulated protein kinase, c-Jun N-terminal protein kinase or p38 upregulates the transcriptional activity of Elk-1, mediated by binding to the mediator of RNA polymerase II transcription (Mediator) and the transcriptional coactivator p300. Strong and extended phosphorylation of Elk-1 attenuates Mediator and p300 recruitment and allows the binding of the mSin3A-histone deacetylase corepressor complex. The subsequent dephosphorylation of Elk-1, catalyzed by the protein phosphatase calcineurin, facilitates the re-SUMOylation of Elk-1, transforming Elk-1 back to a transcriptionally inactive state. Thus, numerous protein–protein interactions control the activation cycle of Elk-1 and are essential for its biological function.

Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

2014 ◽  
Vol 4 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Cellular Systems ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Free Distribution ◽  
Protein Protein Interaction ◽  
Domain Interactions

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Domain-Based Prediction and Analysis of Protein-Protein Interactions

2009 ◽  
pp. 29-44 ◽  
Author(s):  
Tatsuya Akutsu ◽  
Morihiro Hayashida
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Interaction Model ◽  
Protein Interaction Data ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Domain Interactions ◽  
Protein Sequence Data

Many methods have been proposed for inference of protein-protein interactions from protein sequence data. This chapter focuses on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This chapter overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, and linear programmingbased method. This chapter also reviews a simple evolutionary model of protein domains, which yields a scalefree distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

Biotechnology ◽  
2019 ◽  
pp. 406-427
Author(s):  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Interaction Model ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Free Distribution ◽  
Protein Protein Interaction ◽  
Domain Interactions ◽  
Protein Sequence Data

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

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