A Method for Isolation of DNA-Binding Proteins Based on Solubility of DNA-Protein Complexes

2012 ◽  
Vol 19 (10) ◽  
pp. 1071-1075
Author(s):  
Hua Yang ◽  
Huang Li ◽  
Li-qun Rao ◽  
Gui-you Long ◽  
Guo-ping Peng ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Protein Complexes ◽  
Dna Binding Proteins

scholarly journals Phosphorus and sulfur SAD phasing of the nucleic acid-bound DNA-binding domain of interferon regulatory factor 4

2021 ◽  
Vol 77 (7) ◽  
Author(s):  
Alessandro Agnarelli ◽  
Kamel El Omari ◽  
Ramona Duman ◽  
Armin Wagner ◽  
Erika J. Mancini
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Protein Complexes ◽  
Interferon Regulatory Factor ◽  
Dna Binding Proteins ◽  
Binding Domain ◽  
Anomalous Scattering ◽  
Dna Binding Domain ◽  
Regulatory Factor ◽  
Interferon Regulatory Factor 4

Pivotal to the regulation of key cellular processes such as the transcription, replication and repair of DNA, DNA-binding proteins play vital roles in all aspects of genetic activity. The determination of high-quality structures of DNA-binding proteins, particularly those in complexes with DNA, provides crucial insights into the understanding of these processes. The presence in such complexes of phosphate-rich oligonucleotides offers the choice of a rapid method for the routine solution of DNA-binding proteins through the use of long-wavelength beamlines such as I23 at Diamond Light Source. This article reports the use of native intrinsic phosphorus and sulfur single-wavelength anomalous dispersion methods to solve the complex of the DNA-binding domain (DBD) of interferon regulatory factor 4 (IRF4) bound to its interferon-stimulated response element (ISRE). The structure unexpectedly shows three molecules of the IRF4 DBD bound to one ISRE. The sole reliance on native intrinsic anomalous scattering elements that belong to DNA–protein complexes renders the method of general applicability to a large number of such protein complexes that cannot be solved by molecular replacement or by other phasing methods.

scholarly journals Efficient and Specific Targeting of Polycomb Group Proteins Requires Cooperative Interaction between Grainyhead and Pleiohomeotic

2006 ◽  
Vol 26 (4) ◽  
pp. 1434-1444 ◽  
Author(s):  
András Blastyák ◽  
Rakesh K. Mishra ◽  
Francois Karch ◽  
Henrik Gyurkovics
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Protein Complexes ◽  
Dna Binding Proteins ◽  
Polycomb Group ◽  
Cooperative Interaction ◽  
Protein Protein Interaction ◽  
Specific Targeting

ABSTRACT Specific targeting of the protein complexes formed by the Polycomb group of proteins is critically required to maintain the inactive state of a group of developmentally regulated genes. Although the role of DNA binding proteins in this process has been well established, it is still not understood how these proteins target the Polycomb complexes specifically to their response elements. Here we show that the grainyhead gene, which encodes a DNA binding protein, interacts with one such Polycomb response element of the bithorax complex. Grainyhead binds to this element in vitro. Moreover, grainyhead interacts genetically with pleiohomeotic in a transgene-based, pairing-dependent silencing assay. Grainyhead also interacts with Pleiohomeotic in vitro, which facilitates the binding of both proteins to their respective target DNAs. Such interactions between two DNA binding proteins could provide the basis for the cooperative assembly of a nucleoprotein complex formed in vitro. Based on these results and the available data, we propose that the role of DNA binding proteins in Polycomb group-dependent silencing could be described by a model very similar to that of an enhanceosome, wherein the unique arrangement of protein-protein interaction modules exposed by the cooperatively interacting DNA binding proteins provides targeting specificity.

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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