Interactions among the sevenHelicobacter pyloriproteins encoded by the urease gene cluster

2003 ◽  
Vol 284 (1) ◽  
pp. G96-G106 ◽  
Author(s):  
Petra Voland ◽  
David L. Weeks ◽  
Elizabeth A. Marcus ◽  
Christian Prinz ◽  
George Sachs ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Membrane Surface ◽  
Protein Protein Interactions ◽  
Native Page ◽  
Catalytic Subunits ◽  
Membrane Complex ◽  
Dimeric Complexes ◽  
Urea Channel ◽  
H Pylori ◽  
Assembly Proteins

Survival of Helicobacter pylori in acid depends on intrabacterial urease. This urease is a Ni2+-containing oligomeric heterodimer. Regulation of its activity and assembly is important for gastric habitation by this neutralophile. The gene complex encodes catalytic subunits ( ureA/B), an acid-gated urea channel ( ureI), and accessory assembly proteins ( ureE–H). With the use of yeast two-hybrid analysis for determining protein-protein interactions, UreF as bait identified four interacting sequences encoding UreH, whereas UreG as bait detected five UreE sequences. These results were confirmed by coimmunoprecipitation and β-galactosidase assays. Native PAGE immunoblotting of H. pylori inner membranes showed interaction of UreA/B with UreI, whereas UreI deletion mutants lacked this protein interaction. Deletion of ureE–H did not affect this interaction with UreI. Hence, the accessory proteins UreE/G and UreF/H form dimeric complexes and UreA/B form a membrane complex with UreI, perhaps enabling assembly of the urease apoenzyme at the membrane surface and immediate urea access to intrabacterial urease to allow rapid periplasmic neutralization.

scholarly journals Characterization of major chromatin assembly proteins in tetrahymena thermophile using proeomic and molecular evolutionary analyses

2021 ◽  
Author(s):  
Syed N Shah
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Regulation Of Gene Expression ◽  
Chromatin Assembly ◽  
Histone Chaperones ◽  
Protein Protein Interactions ◽  
Selective Forces ◽  
Assembly Proteins

Histones H3/H4 are deposited onto DNA in a replication-dependent or independent fashion by the CAF1 and HIRA protein complexes. Despite the identification of these protein complexes, mechanistic details remain unclear. Recently, we showed that in T. thermophila histone chaperones Nrp1, Asf1 and the Impβ6 importin function together to transport newly synthesized H3/H4 from the cytoplasm to the nucleus. To characterize chromatin assembly proteins in T.thermophila, I used affinity purification combined with mass spectrometry to identify protein-protein interactions of Nrp1, Cac2 subunit of CAF1, HIRA and histone modifying Hat1-complex in T. thermophila. I found that the three-subunit T.thermophila CAF1 complex interacts with Casein Kinase 2 (CKII), possibly accounting for previously reported human CAF1phosphorylation. I also found that Hat2 subunit of HAT1 complex is also shared by CAF1 complex as its Cac3 subunit. This suggests that Hat2/Cac3 might exist in two separate pools of protein complexes. Remarkably, proteomic analysis of Hat2/Cac3 in turn revealed that it forms several complexes with other proteins including SIN3, RXT3, LIN9 and TESMIN, all of which have known roles in the regulation of gene expression. Finally, I asked how selective forces might have impacted on the function of proteins involved in H3/H4 transport. Focusing on NASP which possesses several TPR motifs, I showed that its protein-protein interactions are conserved in T. thermophila. Using molecular evolutionary methods I show that different TPRs in NASP evolve at different rates possibly accounting for the functional diversity observed among different family members.

scholarly journals Protein-Protein Interactions among Helicobacter pylori Cag Proteins

2006 ◽  
Vol 188 (13) ◽  
pp. 4787-4800 ◽  
Author(s):  
Valerie J. Busler ◽  
Victor J. Torres ◽  
Mark S. McClain ◽  
Oscar Tirado ◽  
David B. Friedman ◽  
...  
Keyword(s):  
Helicobacter Pylori ◽  
Protein Interactions ◽  
Secretion System ◽  
Type Iv Secretion ◽  
Type Iv Secretion System ◽  
Protein Protein Interactions ◽  
Chromosomal Dna ◽  
2D Dige ◽  
Type Iv ◽  
H Pylori

ABSTRACT Many Helicobacter pylori isolates contain a 40-kb region of chromosomal DNA known as the cag pathogenicity island (PAI). The risk for development of gastric cancer or peptic ulcer disease is higher among humans infected with cag PAI-positive H. pylori strains than among those infected with cag PAI-negative strains. The cag PAI encodes a type IV secretion system that translocates CagA into gastric epithelial cells. To identify Cag proteins that are expressed by H. pylori during growth in vitro, we compared the proteomes of a wild-type H. pylori strain and an isogenic cag PAI deletion mutant using two-dimensional difference gel electrophoresis (2D-DIGE) in multiple pH ranges. Seven Cag proteins were identified by this approach. We then used a yeast two-hybrid system to detect potential protein-protein interactions among 14 Cag proteins. One heterotypic interaction (CagY/7 with CagX/8) and two homotypic interactions (involving H. pylori VirB11/ATPase and Cag5) were similar to interactions previously reported to occur among homologous components of the Agrobacterium tumefaciens type IV secretion system. Other interactions involved Cag proteins that do not have known homologues in other bacterial species. Biochemical analysis confirmed selected interactions involving five of the proteins that were identified by 2D-DIGE. Protein-protein interactions among Cag proteins are likely to have an important role in the assembly of the H. pylori type IV secretion apparatus.

scholarly journals PCMD-1 bridges the centrioles and the PCM scaffold in C. elegans

2020 ◽  
Author(s):  
Lisa Stenzel ◽  
Judith Mehler ◽  
Alina Schreiner ◽  
Sim Üstüner ◽  
Elisa Zuccoli ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Coiled Coil ◽  
Protein Protein Interactions ◽  
Animal Cells ◽  
C Elegans ◽  
Mitotic Kinase ◽  
Spindle Poles ◽  
Pericentriolar Material ◽  
Translocation Assay ◽  
Assembly Proteins

ABSTRACTCorrect cell division relies on the formation of a bipolar spindle. In animal cells, microtubule nucleation at the spindle poles is facilitated by the pericentriolar material (PCM), which assembles around a pair of centrioles. Although centrioles are essential for PCM assembly, proteins that anchor the PCM to the centrioles are less known. Here we investigate the molecular function of PCMD-1 in bridging the PCM and the centrioles in Caenorhabditis elegans.We demonstrate that centrosomal recruitment of PCMD-1 is dependent on the outer centriolar protein SAS-7. While the most C-terminal part of PCMD-1 is sufficient to target it to the centrosome, the coiled-coil domain promotes its accumulation by facilitating self-interaction. We reveal that PCMD-1 is bridging the centrioles and PCM scaffold through protein-protein interactions with the PCM scaffold protein SPD-5, the mitotic kinase PLK-1 and the centriolar protein SAS-4. Using an ectopic translocation assay, we show that PCMD-1 is able to selectively recruit downstream PCM scaffold components to an ectopic location in the cell, indicating that PCMD-1 is sufficient to anchor the PCM scaffold proteins to the centrioles. Our work suggests that PCMD-1 is an essential functional bridge between the centrioles and the PCM.

scholarly journals A Genome-WideHelicobacter pyloriMorphology Screen Uncovers a Membrane-Spanning Helical Cell Shape Complex

2019 ◽  
Vol 201 (14) ◽  
Author(s):  
Desirée C. Yang ◽  
Kris M. Blair ◽  
Jennifer A. Taylor ◽  
Timothy W. Petersen ◽  
Tate Sessler ◽  
...  
Keyword(s):  
Cell Wall ◽  
Helicobacter Pylori ◽  
Light Scattering ◽  
Protein Interactions ◽  
Cell Morphology ◽  
Cell Shape ◽  
Cross Linking ◽  
Protein Protein Interactions ◽  
Content Type ◽  
H Pylori

ABSTRACTEvident in its name, the gastric pathogenHelicobacter pylorihas a helical cell morphology which facilitates efficient colonization of the human stomach. An improved light-focusing strategy allowed us to robustly distinguish even subtle perturbations ofH. pyloricell morphology by deviations in light-scattering properties measured by flow cytometry. Profiling of an arrayed genome-wide deletion library identified 28 genes that influence different aspects of cell shape, including properties of the helix, cell length or width, cell filament formation, cell shape heterogeneity, and cell branching. Included in this mutant collection were two that failed to form any helical cells, a soluble lytic transglycosylase and a previously uncharacterized putative multipass inner membrane protein HPG27_0728, renamed Csd7. A combination of cell fractionation, mutational, and immunoprecipitation experiments show that Csd7 and Csd2 collaborate to stabilize the Csd1 peptidoglycan (PG) endopeptidase. Thus, bothcsd2andcsd7mutants show the same enhancement of PG tetra-pentapeptide cross-linking ascsd1mutants. Csd7 also links Csd1 with the bactofilin CcmA via protein-protein interactions. Although Csd1 is stable inccmAmutants, these mutants show altered PG tetra-pentapeptide cross-linking, suggesting that Csd7 may directly or indirectly activate as well as stabilize Csd1. These data begin to illuminate a highly orchestrated program to regulate PG modifications that promote helical shape, which includes nine nonessential nonredundant genes required for helical shape and 26 additional genes that further modifyH. pylori’s cell morphology.IMPORTANCEThe stomach ulcer and cancer-causing pathogenHelicobacter pylorihas a helical cell shape which facilitates stomach infection. Using light scattering to measure perturbations of cell morphology, we identified 28 genes that influence different aspects of cell shape. A mutant in a previously uncharacterized protein renamed Csd7 failed to form any helical cells. Biochemical analyses showed that Csd7 collaborates with other proteins to stabilize the cell wall-degrading enzyme Csd1. Csd7 also links Csd1 with a putative filament-forming protein via protein-protein interactions. These data suggest that helical cell shape arises from a highly orchestrated program to regulate cell wall modifications. Targeting of this helical cell shape-promoting program could offer new ways to block infectivity of this important human pathogen.

Quantitative Analysis of Protein–Protein Interactions by Native Page/Fluorimaging

2005 ◽  
Vol 15 (4) ◽  
pp. 469-473 ◽  
Author(s):  
Kylie M. Wagstaff ◽  
Manisha M. Dias ◽  
Gualtiero Alvisi ◽  
David A. Jans
Keyword(s):  
Quantitative Analysis ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Native Page

scholarly journals ACT-SVM: Prediction of Protein-Protein Interactions Based on Support Vector Basis Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Wenzheng Ma ◽  
Yi Cao ◽  
Wenzheng Bao ◽  
Bin Yang ◽  
Yuehui Chen
Keyword(s):  
Protein Interactions ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Svm Classifier ◽  
Protein Protein Interactions ◽  
Svm Model ◽  
Novel Method ◽  
H Pylori ◽  
Almost All ◽  
Human Dataset

The interactions between proteins play important roles in several organisms, and such issue can be involved in almost all activities in the cell. The research of protein-protein interactions (PPIs) can make a huge contribution to the prevention and treatment of diseases. Currently, many prediction methods based on machine learning have been proposed to predict PPIs. In this article, we propose a novel method ACT-SVM that can effectively predict PPIs. The ACT-SVM model maps protein sequences to digital features, performs feature extraction twice on the protein sequence to obtain vector A and descriptor CT, and combines them into a vector. Then, the feature vectors of the protein pair are merged as the input of the support vector machine (SVM) classifier. We utilize nonredundant H. pylori and human dataset to verify the prediction performance of our method. Finally, the proposed method has a prediction accuracy of 0.727897 for H. pylori data and a prediction accuracy of 0.838799 for human dataset. The results demonstrate that this method can be called a stable and reliable prediction model of PPIs.

Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)

2022 ◽  
pp. 154-176
Author(s):  
Zizhe Gao ◽  
Hao Lin
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Rapid Development ◽  
Biological Research ◽  
Data Sets ◽  
Biological Interactions ◽  
Protein Protein Interactions ◽  
H Pylori ◽  
The Impact ◽  
Significant Health

Entering the 21st century, computer science and biological research have entered a stage of rapid development. With the rapid inflow of capital into the field of significant health research, a large number of scholars and investors have begun to focus on the impact of neural network science on biometrics, especially the study of biological interactions. With the rapid development of computer technology, scientists improve or perfect traditional experimental methods. This chapter aims to prove the reliability of the methodology and computing algorithms developed by Satyajit Mahapatra and Ivek Raj Gupta's project team. In this chapter, three datasets take the responsibility to testify the computing algorithms, and they are S. cerevisiae, H. pylori, and Human-B. Anthracis. Among these three sets of data, the S. cerevisiae is the core subset. The result shows 87%, 87.5%, and 89% accuracy and 87%, 86%, and 87% precision for these three data sets, respectively.

scholarly journals Prediction of Protein-Protein Interactions from Protein Sequences by Combining MatPCA Feature Extraction Algorithms and Weighted Sparse Representation Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zheng Wang ◽  
Yang Li ◽  
Zhu-Hong You ◽  
Li-Ping Li ◽  
Xin-Ke Zhan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Protein Interactions ◽  
Biological Activities ◽  
Vital Role ◽  
Computational Method ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Sparse Representation Classifier ◽  
H Pylori

Identifying protein-protein interactions (PPIs) plays a vital role in a number of biological activities such as signal transduction, transcriptional regulation, and apoptosis. Although advances in high-throughput technologies have generated large amounts of PPI data for different species, they only cover a small part of the entire PPI network. Furthermore, traditional experimental methods are generally expensive, time-consuming, tedious, and prone to high false-positive rates. Therefore, to overcome this problem, it is necessary to develop a novel computational method for predicting PPIs. In this article, we propose an efficient computational method to detect protein-protein interactions using only protein sequence information, which integrates the MatPCA feature extraction algorithm and the weighted sparse representation classifier. As a result, when predicting PPIs on yeast, human, and H. pylori datasets, the proposed method achieves superior prediction performance with an average accuracy of 94.55%, 97.48%, and 83.64%, respectively. These experimental results further illustrate that the proposed method is reliable and robust in predicting PPIs, which can be regarded as a useful complement to the experimental method.

Sign in / Sign up

Export Citation Format

Share Document