Structural and binding studies of the C‐terminal domains of yeast TFIIF subunits Tfg1 and Tfg2

Kilpatrick, Adina M.; Koharudin, Leonardus M. I.; Calero, Guillermo; Gronenborn, Angela M.

doi:10.1002/prot.23217

Cited by 8 publications

(2 citation statements)

References 48 publications

(155 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present results may also provide fundamental insight into the biological function and mechanism of WH domains for regulating cellular processes and, in particular transcription, via protein–protein interactions. Intriguingly, although early studies suggested that the C-terminal WH domain of RAP74 may not bind DNA, a very recent study demonstrated that the yeast homologue binds nonspecific DNA with modest affinity . Moreover, although early studies suggested that WH domains primarily function in binding DNA, several studies have now demonstrated that WH and similar domains can also function in protein–protein interactions. ,,,,, In particular, kinetic studies have suggested that the disordered Activation Factor 1 (AF1) of the Androgen Receptor (AR) also binds the same hydrophobic cleft of the RAP74 WH domain .…”

Section: Discussion and Conclusionmentioning

confidence: 99%

Native-Based Simulations of the Binding Interaction Between RAP74 and the Disordered FCP1 Peptide

2013

View full text Add to dashboard Cite

By dephosphorylating the C-terminal domain (CTD) of RNA polymerase II (Pol II), the Transcription Factor IIF (TFIIF)-associating CTD phosphatase (FCP1) performs an essential function in recycling Pol II for subsequent rounds of transcription. The interaction between FCP1 and TFIIF is mediated by the disordered C-terminal tail of FCP1, which folds to form an α-helix upon binding the RAP74 subunit of TFIIF. The present work reports a structure-based simulation study of this interaction between the folded winged-helix domain of RAP74 and the disordered C-terminal tail of FCP1. The comparison of measured and simulated chemical shifts suggests that the FCP1 peptide samples 40-60% of its native helical structure in the unbound disordered ensemble. Free energy calculations suggest that productive binding begins when RAP74 makes hydrophobic contacts with the C-terminal region of the FCP1 peptide. The FCP1 peptide then folds into an amphipathic helix by zipping up the binding interface. The relative plasticity of FCP1 results in a more cooperative binding mechanism, allows for a greater diversity of pathways leading to the bound complex, and may also eliminate the need for "backtracking" from contacts that form out of sequence.

show abstract

Section: Discussion and Conclusionmentioning

confidence: 99%

Native-Based Simulations of the Binding Interaction Between RAP74 and the Disordered FCP1 Peptide

2013

View full text Add to dashboard Cite

show abstract

“…The chemical shifts were previously obtained by Kilpatrick et al from HNCACB and CBCA(CO)NH experiments, and manually assigned to 99% completeness. 23 The chemical shift dataset was divided into sub-sections, randomized, and used for algorithmic analysis.…”

Section: Resultsmentioning

confidence: 99%

Utilizing Machine Learning to Accelerate Automated Assignment of Backbone NMR Data

Venzke¹,

Mascharka²,

Johnson³

et al. 2016

AJUR

View full text Add to dashboard Cite

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for determining three-dimensional structures of biomolecules, including proteins. The protein structure determination process requires measured NMR values to be assigned to specific amino acids in the primary protein sequence. Unfortunately, current manual techniques for the assignment of NMR data are time-consuming and susceptible to error. Many algorithms have been developed to automate the process, with various strengths and weaknesses. The algorithm described in this paper addresses the challenges of previous programs by utilizing machine learning to predict amino acid type, thereby increasing assignment speed. The program also generates place-holders to accommodate missing data and amino acids with unique chemical characteristics, namely proline. Through machine learning and residue-type tagging, the assignment process is greatly sped up, while maintaining high accuracy. KEYWORDS: Chemical Shift; Machine Learning; NMR; Artificial Intelligence; Proteins; Bioinformatics

show abstract

Strukturelle Grundlage der Transkription: 10 Jahre nach dem Chemie‐Nobelpreis

Hantsche

Cramer

2016

Angewandte Chemie

View full text Add to dashboard Cite

In allen lebenden Zellen bildet die Transkription den ersten Schritt der Expression genetischer Information. Ihrer Regulation unterliegen Zelldifferenzierung, Morphogenese sowie Antworten auf Umweltveränderungen. Während der Transkription verwendet das Enzym RNA‐Polymerase DNA als Vorlage, um eine komplementäre RNA‐Kopie von einem Gen herzustellen. Wir beschreiben die Entwicklungen in unserem strukturellen Verständnis der Transkription in Eukaryoten, die in den letzten 10 Jahren nach der Verleihung des Nobelpreises für Chemie an Roger Kornberg im Jahr 2006 erzielt wurden. Eine Aufklärung der Transkriptionsinitiation und ‐elongation zeichnet sich ab, wohingegen die Mechanismen der Transkriptionsregulation weitgehend unverstanden bleiben. In diesem Feld wurden strukturbiologische Hybridmethoden entwickelt, die verschiedene Techniken zur Bestimmung der dreidimensionalen Architektur von großen und transienten makromolekularen Komplexen kombinieren.

show abstract

Structural and binding studies of the C‐terminal domains of yeast TFIIF subunits Tfg1 and Tfg2

Cited by 8 publications

References 48 publications

Native-Based Simulations of the Binding Interaction Between RAP74 and the Disordered FCP1 Peptide

Native-Based Simulations of the Binding Interaction Between RAP74 and the Disordered FCP1 Peptide

Utilizing Machine Learning to Accelerate Automated Assignment of Backbone NMR Data

Strukturelle Grundlage der Transkription: 10 Jahre nach dem Chemie‐Nobelpreis

Contact Info

Product

Resources

About