The Basic-Region Leucine-Zipper Family of DNA Binding Proteins

1992 ◽  
pp. 82-101 ◽  
Author(s):  
J. C. Hu ◽  
R. T. Sauer
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Leucine Zipper ◽  
Dna Binding Proteins ◽  
Basic Region

The leucine zipper: a hypothetical structure common to a new class of DNA binding proteins

Science ◽  
1988 ◽  
Vol 240 (4860) ◽  
pp. 1759-1764 ◽  
Author(s):  
WH Landschulz ◽  
PF Johnson ◽  
SL McKnight
Keyword(s):  
Amino Acid ◽  
Dna Binding ◽  
Binding Proteins ◽  
Leucine Zipper ◽  
Regulatory Protein ◽  
Alpha Helix ◽  
Dna Binding Proteins ◽  
Amino Acid Sequences ◽  
Helical Conformation ◽  
Hypothetical Structure

A 30-amino-acid segment of C/EBP, a newly discovered enhancer binding protein, shares notable sequence similarity with a segment of the cellular Myc transforming protein. Display of these respective amino acid sequences on an idealized alpha helix revealed a periodic repetition of leucine residues at every seventh position over a distance covering eight helical turns. The periodic array of at least four leucines was also noted in the sequences of the Fos and Jun transforming proteins, as well as that of the yeast gene regulatory protein, GCN4. The polypeptide segments containing these periodic arrays of leucine residues are proposed to exist in an alpha-helical conformation, and the leucine side chains extending from one alpha helix interdigitate with those displayed from a similar alpha helix of a second polypeptide, facilitating dimerization. This hypothetical structure is referred to as the "leucine zipper," and it may represent a characteristic property of a new category of DNA binding proteins.

Identification of a novel dimer stabilization region in a plant bZIP transcription activator

1992 ◽  
Vol 12 (11) ◽  
pp. 4809-4816
Author(s):  
F Katagiri ◽  
K Seipel ◽  
N H Chua
Keyword(s):  
Dna Binding ◽  
Binding Affinity ◽  
Binding Proteins ◽  
Leucine Zipper ◽  
Dna Binding Proteins ◽  
Transcription Activator ◽  
Bzip Domain ◽  
Dna Binding Affinity ◽  
Necessary And Sufficient

We have carried out deletion analyses of a tobacco transcription activator, TGA1a, in order to define its functional domains. TGA1a belongs to the basic-region-leucine zipper (bZIP) class of DNA-binding proteins. Like other proteins of this class, it binds to its target DNA as a dimer, and its bZIP domain is necessary and sufficient for specific DNA binding. A mutant polypeptide containing the bZIP domain alone, however, shows a lower DNA-binding affinity than the full-length TGA1a. The C-terminal portion of TGA1a, which is essential for the higher DNA-binding affinity, contains a polypeptide region that can stabilize dimeric forms of the protein. This polypeptide region is designated the dimer stabilization (DS) region. Under our in vitro conditions, TGA1a derivatives with the DS region and those without the region do not form a detectable mixed dimer. This result indicates that in addition to the leucine zipper, the DS region can serve as another determinant of the dimerization specificity of TGA1a. In fact, the DS region, when fused to another bZIP protein, C/EBP, can inhibit dimer formation between the fusion protein and native C/EBP, whereas each of these can form homodimers. Such a portable determinant of dimerization specificity has potential application in studies of DNA-binding proteins as well as in biotechnology.

scholarly journals Identification of a novel dimer stabilization region in a plant bZIP transcription activator.

1992 ◽  
Vol 12 (11) ◽  
pp. 4809-4816 ◽  
Author(s):  
F Katagiri ◽  
K Seipel ◽  
N H Chua
Keyword(s):  
Dna Binding ◽  
Binding Affinity ◽  
Binding Proteins ◽  
Leucine Zipper ◽  
Dna Binding Proteins ◽  
Transcription Activator ◽  
Bzip Domain ◽  
Dna Binding Affinity ◽  
Necessary And Sufficient

We have carried out deletion analyses of a tobacco transcription activator, TGA1a, in order to define its functional domains. TGA1a belongs to the basic-region-leucine zipper (bZIP) class of DNA-binding proteins. Like other proteins of this class, it binds to its target DNA as a dimer, and its bZIP domain is necessary and sufficient for specific DNA binding. A mutant polypeptide containing the bZIP domain alone, however, shows a lower DNA-binding affinity than the full-length TGA1a. The C-terminal portion of TGA1a, which is essential for the higher DNA-binding affinity, contains a polypeptide region that can stabilize dimeric forms of the protein. This polypeptide region is designated the dimer stabilization (DS) region. Under our in vitro conditions, TGA1a derivatives with the DS region and those without the region do not form a detectable mixed dimer. This result indicates that in addition to the leucine zipper, the DS region can serve as another determinant of the dimerization specificity of TGA1a. In fact, the DS region, when fused to another bZIP protein, C/EBP, can inhibit dimer formation between the fusion protein and native C/EBP, whereas each of these can form homodimers. Such a portable determinant of dimerization specificity has potential application in studies of DNA-binding proteins as well as in biotechnology.

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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