scholarly journals Novel clustering of Sp1 transcription factor binding sites at the transcription initiation site of the human muscle phosphofructokinase P1 promoter

1994 ◽  
Vol 22 (23) ◽  
pp. 5085-5092 ◽  
Author(s):  
Jennifer L. Johnson ◽  
Alan McLachlan
1999 ◽  
Vol 340 (2) ◽  
pp. 513-518 ◽  
Author(s):  
C. Arnold SPEK ◽  
Rogier M. BERTINA ◽  
Pieter H. REITSMA

Recent studies on the regulation of protein C gene transcription revealed the presence of three transcription-factor binding sites in the close proximity to the transcription start site. The proximal 40 bp upstream of the transcription-initiation site contain two, partly overlapping, binding sites for the liver-enriched hepatocyte nuclear factor (HNF)-3 and one binding site to which HNF-1 and HNF-6 bind in a mutually exclusive manner. In order to examine the functionality of the tight alignment of transcription-factor binding sites around the transcription-initiation site, we performed insertional mutagenesis experiments. Sequences were inserted at position -21, separating both HNF-3 binding sites from the HNF-1-HNF-6 binding site, and position -5, separating the HNF-3-HNF-1-HNF-6 complex from the transcription start site. All insertions were made in the context of the protein C gene -386/+107 promoter region and tested for activity by transient transfection experiments. Insertions at position -21 resulted in a combined distance- and DNA-turn-dependent increase in protein C gene expression. Insertions of variable length at position -5 decreased protein C gene expression in a DNA-turn-dependent manner. However, this turn-dependent decrease was accompanied by a distance-dependent increase in promoter activity. This is the first report in which changing the spacing between adjacent transcription-factor binding sites results in enhanced transcription, indicating the submaximal alignment of promoter elements in the wild-type protein C gene promoter region.


2021 ◽  
Vol 11 (11) ◽  
pp. 5123
Author(s):  
Maiada M. Mahmoud ◽  
Nahla A. Belal ◽  
Aliaa Youssif

Transcription factors (TFs) are proteins that control the transcription of a gene from DNA to messenger RNA (mRNA). TFs bind to a specific DNA sequence called a binding site. Transcription factor binding sites have not yet been completely identified, and this is considered to be a challenge that could be approached computationally. This challenge is considered to be a classification problem in machine learning. In this paper, the prediction of transcription factor binding sites of SP1 on human chromosome1 is presented using different classification techniques, and a model using voting is proposed. The highest Area Under the Curve (AUC) achieved is 0.97 using K-Nearest Neighbors (KNN), and 0.95 using the proposed voting technique. However, the proposed voting technique is more efficient with noisy data. This study highlights the applicability of the voting technique for the prediction of binding sites, and highlights the outperformance of KNN on this type of data. The study also highlights the significance of using voting.


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