scholarly journals Yeast transcription co-activator Sub1 and its human homolog PC4 preferentially bind to G-quadruplex DNA

2015 ◽  
Vol 51 (33) ◽  
pp. 7242-7244 ◽  
Author(s):  
Jun Gao ◽  
Boris L. Zybailov ◽  
Alicia K. Byrd ◽  
Wezley C. Griffin ◽  
Shubeena Chib ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Human Homolog ◽  
Dna Metabolism ◽  
Quadruplex Dna ◽  
G4 Dna ◽  
G Quadruplex

DNA binding proteins Sub1 and PC4 preferentially bind to G-quadruplex DNA, providing a new link between DNA metabolism and G4-DNA.

scholarly journals Common Aspects of G-Quadruplex Destabilization among Helicases and Single Stranded DNA Binding Proteins

2015 ◽  
Vol 108 (2) ◽  
pp. 399a-400a
Author(s):  
Jagat B. Budhathoki ◽  
Sujay Ray ◽  
Pavel Janscak ◽  
Jaya Yodh ◽  
Hamza Balci
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Single Stranded Dna ◽  
G Quadruplex

Human Pif1 helicase is a G-quadruplex DNA-binding protein with G-quadruplex DNA-unwinding activity

2010 ◽  
Vol 430 (1) ◽  
pp. 119-128 ◽  
Author(s):  
Cyril M. Sanders
Keyword(s):  
Dna Binding ◽  
Dna Sequences ◽  
Genome Stability ◽  
Dna Helicase ◽  
Dna Unwinding ◽  
Helicase Domain ◽  
Quadruplex Dna ◽  
G4 Dna ◽  
G Quadruplex ◽  
Pif1 Helicase

Pif1 proteins are helicases that in yeast are implicated in the maintenance of genome stability. One activity of Saccharomyces cerevisiae Pif1 is to stabilize DNA sequences that could otherwise form deleterious G4 (G-quadruplex) structures by acting as a G4 resolvase. The present study shows that human Pif1 (hPif1, nuclear form) is a G4 DNA-binding and resolvase protein and that these activities are properties of the conserved helicase domain (amino acids 206–620 of 641, hPifHD). hPif1 preferentially bound synthetic G4 DNA relative to ssDNA (single-stranded DNA), dsDNA (double-stranded DNA) and a partially single-stranded duplex DNA helicase substrate. G4 DNA unwinding, but not binding, required an extended (>10 nucleotide) 5′ ssDNA tail, and in competition assays, G4 DNA was an ineffective suppressor of helicase activity compared with ssDNA. These results suggest a distinction between the determinants of G4 DNA binding and the ssDNA interactions required for helicase action and that hPif1 may act on G4 substrates by binding alone or as a resolvase. Human Pif1 could therefore have a role in processing G4 structures that arise in the single-stranded nucleic acid intermediates formed during DNA replication and gene expression.

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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