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Towards biological characters of interactions between transcription factors and their DNA targets in mammals.

Zheng G, Liu Q, Ding G, Wei C, Li Y - BMC Genomics (2012)

Bottom Line: Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions.In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. zhenggy@sibs.ac.cn

ABSTRACT

Background: In post-genomic era, the study of transcriptional regulation is pivotal to decode genetic information. Transcription factors (TFs) are central proteins for transcriptional regulation, and interactions between TFs and their DNA targets (TFBSs) are important for downstream genes' expression. However, the lack of knowledge about interactions between TFs and TFBSs is still baffling people to investigate the mechanism of transcription.

Results: To expand the knowledge about interactions between TFs and TFBSs, three biological features (sequence feature, structure feature, and evolution feature) were utilized to build TFBS identification models for studying binding preference between TFs and their DNA targets in mammals. Results show that each feature does have fairly well performance to capture TFBSs, and the hybrid model combined all three features is more robust for TFBS identification. Subsequently, correspondence between TFs and their TFBSs was investigated to explore interactions among them in mammals. Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.

Conclusions: Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions. In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

Show MeSH
Distribution of accuracy measurement for different models. Panel (a): the histogram of the sequence model and the hybrid model. The green and red rectangle represents the former and the latter’s accuracy frequency respectively. Panel (b): the histogram of the structure model and the hybrid model. The green and red rectangle represents the former and the latter’s accuracy frequency respectively.
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Figure 3: Distribution of accuracy measurement for different models. Panel (a): the histogram of the sequence model and the hybrid model. The green and red rectangle represents the former and the latter’s accuracy frequency respectively. Panel (b): the histogram of the structure model and the hybrid model. The green and red rectangle represents the former and the latter’s accuracy frequency respectively.

Mentions: In order to survey power of the hybrid model further, we investigated frequency distribution of accuracy measurement for the hybrid model and the best single feature model in the two datasets (Figure 3). In dataset 1, the hybrid model was compared with the sequence model. While in dataset 2, the hybrid model and the structure model were compared. As shown in Figure 3, for accuracy, values of the hybrid model were more concentrated in high score region than the single feature model. That outcome demonstrated that the hybrid model was more robust than the single feature model.


Towards biological characters of interactions between transcription factors and their DNA targets in mammals.

Zheng G, Liu Q, Ding G, Wei C, Li Y - BMC Genomics (2012)

Distribution of accuracy measurement for different models. Panel (a): the histogram of the sequence model and the hybrid model. The green and red rectangle represents the former and the latter’s accuracy frequency respectively. Panel (b): the histogram of the structure model and the hybrid model. The green and red rectangle represents the former and the latter’s accuracy frequency respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472306&req=5

Figure 3: Distribution of accuracy measurement for different models. Panel (a): the histogram of the sequence model and the hybrid model. The green and red rectangle represents the former and the latter’s accuracy frequency respectively. Panel (b): the histogram of the structure model and the hybrid model. The green and red rectangle represents the former and the latter’s accuracy frequency respectively.
Mentions: In order to survey power of the hybrid model further, we investigated frequency distribution of accuracy measurement for the hybrid model and the best single feature model in the two datasets (Figure 3). In dataset 1, the hybrid model was compared with the sequence model. While in dataset 2, the hybrid model and the structure model were compared. As shown in Figure 3, for accuracy, values of the hybrid model were more concentrated in high score region than the single feature model. That outcome demonstrated that the hybrid model was more robust than the single feature model.

Bottom Line: Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions.In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. zhenggy@sibs.ac.cn

ABSTRACT

Background: In post-genomic era, the study of transcriptional regulation is pivotal to decode genetic information. Transcription factors (TFs) are central proteins for transcriptional regulation, and interactions between TFs and their DNA targets (TFBSs) are important for downstream genes' expression. However, the lack of knowledge about interactions between TFs and TFBSs is still baffling people to investigate the mechanism of transcription.

Results: To expand the knowledge about interactions between TFs and TFBSs, three biological features (sequence feature, structure feature, and evolution feature) were utilized to build TFBS identification models for studying binding preference between TFs and their DNA targets in mammals. Results show that each feature does have fairly well performance to capture TFBSs, and the hybrid model combined all three features is more robust for TFBS identification. Subsequently, correspondence between TFs and their TFBSs was investigated to explore interactions among them in mammals. Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.

Conclusions: Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions. In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.

Show MeSH