The yeast ARGRII regulatory protein has homology with various RNases and DNA binding proteins

1988 ◽  
Vol 211 (1) ◽  
pp. 102-105 ◽  
Author(s):  
Francine Messenguy ◽  
Evelyne Dubois
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Regulatory Protein ◽  
Dna Binding Proteins

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.

Structural studies of protein–nucleic acid interaction: the sources of sequence-specific binding

1990 ◽  
Vol 23 (3) ◽  
pp. 205-280 ◽  
Author(s):  
Thomas A. Steitz
Keyword(s):  
Dna Binding ◽  
Crystal Structures ◽  
Binding Proteins ◽  
Regulatory Protein ◽  
Dna Binding Proteins ◽  
Structural Studies ◽  
Klenow Fragment ◽  
Proteolytic Fragment ◽  
Repressor Protein ◽  
E Coli

Structural studies of DNA-binding proteins and their complexes with DNA have proceeded at an accelerating pace in recent years due to important technical advances in molecular genetics, DNA synthesis, protein crystallography and nuclear magnetic resonance. The last major review on this subject by Pabo & Sauer (1984) summarized the structural and functional studies of the three sequence-specific DNA-binding proteins whose crystal structures were then known, the E. coli catabolite gene activator protein (CAP) (McKay & Steitz, 1981; McKay et al. 1982; Weber & Steitz, 1987), a cro repressor from phage λ (Anderson et al. 1981), and the DNA-binding proteolytic fragment of λcI repressor protein (Pabo & Lewis, 1982) Although crystallographic studies of the E. coli lac repressor protein were initiated as early as 1971 when it was the only regulatory protein available in sufficient quantities for structural studies (Steitz et al. 1974), little was established about the structural aspects of DNA-binding proteins until the structure of CAP was determined in 1980 followed shortly thereafter by the structure of λcro repressor and subsequently that of the λ repressor fragment. There are now determined at high resolution the crystal structures of seven prokaryotic gene regulatory proteins or fragments [CAP, λcro, λcI repressor fragment, 434 repressor fragment (Anderson et al. 1987), 434 cro repressor (Wolberger et al. 1988), E. coli trp repressor (Schevitz et al. 1985), E. coli met repressor (Rafferty et al. 1989)], EcoR I restriction endonuclease (McClarin et al. 1986), DNAse I (Suck & Ofner, 1986), the catalytic domain of γδ resolvase (Hatfull et al. 1989) and two sequence-independent double-stranded DNA-binding proteins [the Klenow fragment of E. coli DNA polymerase I (Ollis et al. 1985) and the E. coli Hu protein (Tanaka et al., 1984)].

scholarly journals Collaborative Competition Mechanism for Gene Activation In Vivo

2003 ◽  
Vol 23 (5) ◽  
pp. 1623-1632 ◽  
Author(s):  
Joanna A. Miller ◽  
Jonathan Widom
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Regulatory Protein ◽  
Gene Activation ◽  
Dna Binding Proteins ◽  
Regulatory Proteins ◽  
Target Sites ◽  
Collaborative Competition

ABSTRACT The mechanism by which gene regulatory proteins gain access to their DNA target sites is not known. In vitro, binding is inherently cooperative between arbitrary DNA binding proteins whose target sites are located within the same nucleosome. We refer to such competition-based cooperativity as collaborative competition. Here we show that arbitrarily chosen foreign DNA binding proteins, LexA and Tet repressor, cooperate with an adjacently binding endogenous activator protein, Gcn4, to coactivate expression of chromosomal reporter genes in Saccharomyces cerevisiae. Coactivation requires that the cooperating target sites be within a nucleosome-length distance; it leads to increased occupancy by Gcn4 at its binding site; and it requires both Gcn5 and Swi/Snf which, at an endogenous Gcn4-dependent promoter, act subsequent to Gcn4 binding. These results imply that collaborative competition contributes to gene regulation in vivo. They further imply that, even in the presence of the cell's full wild-type complement of chromatin remodeling factors, competition of regulatory proteins with histone octamer for access to regulatory target sites remains a quantitative determinant of gene expression levels. We speculate that initial target site recognition and binding may occur via spontaneous nucleosomal site exposure, with remodeling factor action required downstream to lock in higher levels of regulatory protein occupancy.

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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