Positively Charged DNA-Binding Proteins Cause Apparent Cell Membrane Translocation

2002 ◽  
Vol 291 (2) ◽  
pp. 367-371 ◽  
Author(s):  
Mathias Lundberg ◽  
Magnus Johansson
Keyword(s):  
Dna Binding ◽  
Cell Membrane ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Membrane Translocation ◽  
Positively Charged

scholarly journals A Fast Signal–Induced Activation of Poly(Adp-Ribose) Polymerase

2000 ◽  
Vol 150 (2) ◽  
pp. 293-308 ◽  
Author(s):  
S. Homburg ◽  
L. Visochek ◽  
N. Moran ◽  
F. Dantzer ◽  
E. Priel ◽  
...  
Keyword(s):  
Dna Binding ◽  
Cell Membrane ◽  
Binding Proteins ◽  
Nuclear Protein ◽  
Dna Binding Proteins ◽  
Alternative Mechanism ◽  
Dna Breaks ◽  
Downstream Target ◽  
Inositol 1,4,5 Trisphosphate ◽  
Fast Activation

We present the first evidence for a fast activation of the nuclear protein poly(ADP-ribose) polymerase (PARP) by signals evoked in the cell membrane, constituting a novel mode of signaling to the cell nucleus. PARP, an abundant, highly conserved, chromatin-bound protein found only in eukaryotes, exclusively catalyzes polyADP-ribosylation of DNA-binding proteins, thereby modulating their activity. Activation of PARP, reportedly induced by formation of DNA breaks, is involved in DNA transcription, replication, and repair. Our findings demonstrate an alternative mechanism: a fast activation of PARP, evoked by inositol 1,4,5,-trisphosphate–Ca2+ mobilization, that does not involve DNA breaks. These findings identify PARP as a novel downstream target of phospholipase C, and unveil a novel fast signal–induced modification of DNA-binding proteins by polyADP-ribosylation.

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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