scholarly journals Investigation of Changes in Tetracycline Repressor Binding upon Mutations in the Tetracycline Operator

2014 ◽  
Vol 59 (10) ◽  
pp. 3167-3176 ◽  
Author(s):  
Dan S. Bolintineanu ◽  
Katherine Volzing ◽  
Victor Vivcharuk ◽  
Abdallah Sayyed-Ahmad ◽  
Poonam Srivastava ◽  
...  
Keyword(s):  
Tetracycline Repressor

scholarly journals Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites

2020 ◽  
Vol 48 (22) ◽  
pp. 12604-12617
Author(s):  
Pengpeng Long ◽  
Lu Zhang ◽  
Bin Huang ◽  
Quan Chen ◽  
Haiyan Liu
Keyword(s):  
Genome Sequence ◽  
Energy Function ◽  
Structural Information ◽  
Structural Data ◽  
P Values ◽  
A Genome ◽  
Z Scores ◽  
Transcription Regulators ◽  
Dna Specificity ◽  
Tetracycline Repressor

Abstract We report an approach to predict DNA specificity of the tetracycline repressor (TetR) family transcription regulators (TFRs). First, a genome sequence-based method was streamlined with quantitative P-values defined to filter out reliable predictions. Then, a framework was introduced to incorporate structural data and to train a statistical energy function to score the pairing between TFR and TFR binding site (TFBS) based on sequences. The predictions benchmarked against experiments, TFBSs for 29 out of 30 TFRs were correctly predicted by either the genome sequence-based or the statistical energy-based method. Using P-values or Z-scores as indicators, we estimate that 59.6% of TFRs are covered with relatively reliable predictions by at least one of the two methods, while only 28.7% are covered by the genome sequence-based method alone. Our approach predicts a large number of new TFBs which cannot be correctly retrieved from public databases such as FootprintDB. High-throughput experimental assays suggest that the statistical energy can model the TFBSs of a significant number of TFRs reliably. Thus the energy function may be applied to explore for new TFBSs in respective genomes. It is possible to extend our approach to other transcriptional factor families with sufficient structural information.

scholarly journals Modular organisation of inducer recognition and allostery in the tetracycline repressor

FEBS Journal ◽  
2016 ◽  
Vol 283 (11) ◽  
pp. 2102-2114 ◽  
Author(s):  
Sebastiaan Werten ◽  
Julia Schneider ◽  
Gottfried Julius Palm ◽  
Winfried Hinrichs
Keyword(s):  
Modular Organisation ◽  
Tetracycline Repressor

Tetracycline Repressor Acts as a Molecular Switch Regulated by Tetracycline Binding

1999 ◽  
pp. 349-365 ◽  
Author(s):  
W. Hinrichs ◽  
P. Orth ◽  
C. Kisker ◽  
D. Schnappinger ◽  
W. Hillen ◽  
...  
Keyword(s):  
Molecular Switch ◽  
Tetracycline Repressor

scholarly journals Development of a Self-assembling Nuclear Targeting Vector System Based on the Tetracycline Repressor Protein

2003 ◽  
Vol 279 (7) ◽  
pp. 5555-5564 ◽  
Author(s):  
Laurence Vaysse ◽  
Richard Harbottle ◽  
Brian Bigger ◽  
Anna Bergau ◽  
Oleg Tolmachov ◽  
...  
Keyword(s):  
Vector System ◽  
Nuclear Targeting ◽  
Repressor Protein ◽  
Self Assembling ◽  
Targeting Vector ◽  
Tetracycline Repressor

The Tetracycline Repressor—A Paradigm for a Biological Switch

2000 ◽  
Vol 39 (12) ◽  
pp. 2042-2052 ◽  
Author(s):  
Wolfram Saenger ◽  
Peter Orth ◽  
Caroline Kisker ◽  
Wolfgang Hillen ◽  
Winfried Hinrichs
Keyword(s):  
Tetracycline Repressor

Molecular Dynamics Simulations of the Tetracycline-repressor Protein: The Mechanism of Induction

2006 ◽  
Vol 359 (4) ◽  
pp. 1125-1136 ◽  
Author(s):  
Harald Lanig ◽  
Olaf G. Othersen ◽  
Frank R. Beierlein ◽  
Ute Seidel ◽  
Timothy Clark
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Repressor Protein ◽  
Dynamics Simulations ◽  
Tetracycline Repressor
Sign in / Sign up

Export Citation Format

Share Document