scholarly journals Systematic identification of cooperation between DNA binding proteins in 3D space

2016 ◽  
Author(s):  
Kai Zhang ◽  
Nan Li ◽  
Wei Wang
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Structure Identification ◽  
Chromosomal Structure ◽  
3D Space ◽  
Key Factor ◽  
Cell Type Specific ◽  
Cis And Trans ◽  
Systematic Identification

Cooperation between DNA-binding proteins (DBPs) such as transcription factors and chromatin remodeling enzymes plays a pivotal role in regulating gene expression and other biological processes. Such cooperation is often via interaction between DBPs that bind to loci located distal in the linear genome but close in the 3D space, referred as trans-cooperation. Due to the lack of 3D chromosomal structure, identification of DBP cooperation has been limited to those binding to neighbor regions in the linear genome, referred as cis-cooperation. Here we present the first study that integrates protein ChIP-seq and Hi-C data to systematically identify both cis- and trans-cooperation between DBPs. We developed a new network model that allows identification of cooperation between multiple DBPs and reveals cell type specific or independent regulations. Particularly interesting, we have retrieved many known and previously unknown trans-cooperation between DBPs in the chromosomal loops that may be a key factor for influencing 3D chromosomal structure. The software is available at http://wanglab.ucsd.edu/star/DBPnet/index.html.

Cell-type-specific expression of the rat thyroperoxidase promoter indicates common mechanisms for thyroid-specific gene expression

1992 ◽  
Vol 12 (2) ◽  
pp. 576-588
Author(s):  
H Francis-Lang ◽  
M Price ◽  
M Polycarpou-Schwarz ◽  
R Di Lauro
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Proximal Region ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
Rat Thyroid

A 420-bp fragment from the 5' end of the rat thyroperoxidase (TPO) gene was fused to a luciferase reporter and shown to direct cell-type-specific expression when transfected into rat thyroid FRTL-5 cells. Analysis of this DNA fragment revealed four regions of the promoter which interact with DNA-binding proteins present in FRTL-5 cells. Mutation of the DNA sequence within any of these regions reduced TPO promoter activity. The trans-acting factors binding to these sequences were compared with thyroid transcription factor 1 (TTF-1) and TTF-2, previously identified as transcriptional activators of another thyroid-specific gene, the thyroglobulin (Tg) gene. Purified TTF-1 binds to three regions of TPO which are protected by FRTL-5 proteins. Two of the binding sites overlap with recognition sites for other DNA-binding proteins. One TTF-1 site can also bind a protein (UFB) present in the nuclei of both expressing and nonexpressing cells. TTF-1 binding to the proximal region overlaps with that for a novel protein present in FRTL-5 cells which can also recognize the promoter-proximal region of Tg. Using a combination of techniques, the factor binding to the fourth TPO promoter site was shown to be TTF-2. We conclude, therefore, that the FRTL-5-specific expression of two thyroid restricted genes, encoding TPO and Tg, relies on a combination of the same trans-acting factors present in thyroid cells.

scholarly journals Cell-type-specific expression of the rat thyroperoxidase promoter indicates common mechanisms for thyroid-specific gene expression.

1992 ◽  
Vol 12 (2) ◽  
pp. 576-588 ◽  
Author(s):  
H Francis-Lang ◽  
M Price ◽  
M Polycarpou-Schwarz ◽  
R Di Lauro
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Proximal Region ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
Rat Thyroid

A 420-bp fragment from the 5' end of the rat thyroperoxidase (TPO) gene was fused to a luciferase reporter and shown to direct cell-type-specific expression when transfected into rat thyroid FRTL-5 cells. Analysis of this DNA fragment revealed four regions of the promoter which interact with DNA-binding proteins present in FRTL-5 cells. Mutation of the DNA sequence within any of these regions reduced TPO promoter activity. The trans-acting factors binding to these sequences were compared with thyroid transcription factor 1 (TTF-1) and TTF-2, previously identified as transcriptional activators of another thyroid-specific gene, the thyroglobulin (Tg) gene. Purified TTF-1 binds to three regions of TPO which are protected by FRTL-5 proteins. Two of the binding sites overlap with recognition sites for other DNA-binding proteins. One TTF-1 site can also bind a protein (UFB) present in the nuclei of both expressing and nonexpressing cells. TTF-1 binding to the proximal region overlaps with that for a novel protein present in FRTL-5 cells which can also recognize the promoter-proximal region of Tg. Using a combination of techniques, the factor binding to the fourth TPO promoter site was shown to be TTF-2. We conclude, therefore, that the FRTL-5-specific expression of two thyroid restricted genes, encoding TPO and Tg, relies on a combination of the same trans-acting factors present in thyroid cells.

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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