Uranyl Acetate as a Direct Inhibitor of DNA-Binding Proteins

2007 ◽  
Vol 20 (5) ◽  
pp. 784-789 ◽  
Author(s):  
Wendy J. Hartsock ◽  
Jennifer D. Cohen ◽  
David J. Segal
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Uranyl Acetate ◽  
Inhibitor Of Dna Binding

scholarly journals Bone Morphogenetic Protein 2 Stimulates Noncanonical SMAD2/3 Signaling via the BMP Type 1A Receptor in Gonadotrope-Like Cells: Implications for FSH Synthesis

Endocrinology ◽  
2014 ◽  
Vol 155 (5) ◽  
pp. 1970-1981 ◽  
Author(s):  
Ying Wang ◽  
Catherine C. Ho ◽  
EunJin Bang ◽  
Carlis A. Rejon ◽  
Vanessa Libasci ◽  
...  
Keyword(s):  
Dna Binding ◽  
Bone Morphogenetic Proteins ◽  
Intracellular Signaling ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Bone Morphogenetic Protein 2 ◽  
Type I ◽  
Paracrine Factors ◽  
Rich Domain ◽  
Inhibitor Of Dna Binding

FSH is an essential regulator of mammalian reproduction. Its synthesis by pituitary gonadotrope cells is regulated by multiple endocrine and paracrine factors, including TGFβ superfamily ligands, such as the activins and inhibins. Activins stimulate FSH synthesis via transcriptional regulation of its β-subunit gene (Fshb). More recently, bone morphogenetic proteins (BMPs) were shown to stimulate murine Fshb transcription alone and in synergy with activins. BMP2 signals via its canonical type I receptor, BMPR1A (or activin receptor-like kinase 3 [ALK3]), and SMAD1 and SMAD5 to stimulate transcription of inhibitor of DNA binding proteins. Inhibitor of DNA binding proteins then potentiate the actions of activin-stimulated SMAD3 to regulate the Fshb gene in the gonadotrope-like LβT2 cell line. Here, we report the unexpected observation that BMP2 also stimulates the SMAD2/3 pathway in these cells and that it does so directly via ALK3. Indeed, this novel, noncanonical ALK3 activity is completely independent of ALK4, ALK5, and ALK7, the type I receptors most often associated with SMAD2/3 pathway activation. Induction of the SMAD2/3 pathway by ALK3 is dependent upon its own previous activation by associated type II receptors, which phosphorylate conserved serine and threonine residues in the ALK3 juxtamembrane glycine-serine-rich domain. ALK3 signaling via SMAD3 is necessary for the receptor to stimulate Fshb transcription, whereas its activation of the SMAD1/5/8 pathway alone is insufficient. These data challenge current dogma that ALK3 and other BMP type I receptors signal via SMAD1, SMAD5, and SMAD8 and not SMAD2 or SMAD3. Moreover, they suggest that BMPs and activins may use similar intracellular signaling mechanisms to activate the murine Fshb promoter in immortalized gonadotrope-like cells.

Expression and regulation of inhibitor of DNA binding proteins ID1, ID2, ID3, and ID4 at the maternal-conceptus interface in pigs

2018 ◽  
Vol 108 ◽  
pp. 46-55 ◽  
Author(s):  
Jisoo Han ◽  
Heewon Seo ◽  
Yohan Choi ◽  
Choongdeok Lee ◽  
Meong Il Kim ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Inhibitor Of Dna Binding

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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