Structure and function of bovine growth hormone bovine growth hormone as an experimental model for studies of protein-protein interactions

1997 ◽  
Vol 688 (2) ◽  
pp. 161-177 ◽  
Author(s):  
Camillo Secchi ◽  
Vitaliano Borromeo
Keyword(s):  
Growth Hormone ◽  
Experimental Model ◽  
Protein Interactions ◽  
Structure And Function ◽  
Bovine Growth Hormone ◽  
Protein Protein Interactions ◽  
And Function

scholarly journals The Role of Meningococcal Porin B in Protein-Protein Interactions with Host Cells

2018 ◽  
Vol 62 (1) ◽  
pp. 52-58
Author(s):  
E. Káňová ◽  
I. Jiménez-Munguía ◽  
Ľ. Čomor ◽  
Z. Tkáčová ◽  
I. Širochmanová ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Cell Signalling ◽  
Structure And Function ◽  
Host Cells ◽  
Protein Protein Interactions ◽  
Receptor Interactions ◽  
And Function ◽  
Fatal Sepsis ◽  
Porin B

Abstract Neisseria meningitidis is a Gram-negative diplococcus responsible for bacterial meningitis and fatal sepsis. Ligand-receptor interactions are one of the main steps in the development of neuroinvasion. Porin B (PorB), neisserial outer membrane protein (ligand), binds to host receptors and triggers many cell signalling cascades allowing the meningococcus to damage the host cells or induce immune cells responses via the TLR2-dependent mechanisms. In this paper, we present a brief review of the structure and function of PorB.

scholarly journals Survey of Natural Language Processing Techniques in Bioinformatics

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiqiang Zeng ◽  
Hua Shi ◽  
Yun Wu ◽  
Zhiling Hong
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Protein Interactions ◽  
Noncoding Rna ◽  
Structure And Function ◽  
Protein Protein Interactions ◽  
And Function ◽  
Processing Techniques

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

scholarly journals Protein interactions and consensus clustering analysis uncover insights into herpesvirus virion structure and function relationships

PLoS Biology ◽  
2019 ◽  
Vol 17 (6) ◽  
pp. e3000316 ◽  
Author(s):  
Anna Hernández Durán ◽  
Todd M. Greco ◽  
Benjamin Vollmer ◽  
Ileana M. Cristea ◽  
Kay Grünewald ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Clustering Analysis ◽  
Structure And Function ◽  
Consensus Clustering ◽  
Virion Structure ◽  
And Function

scholarly journals Transactivation Functions of the N-Terminal Domains of Nuclear Hormone Receptors: Protein Folding and Coactivator Interactions

2003 ◽  
Vol 17 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Raj Kumar ◽  
E. Brad Thompson
Keyword(s):  
Dna Binding ◽  
Protein Interactions ◽  
Hormone Receptors ◽  
Nuclear Hormone Receptor ◽  
Binding Domain ◽  
Dna Binding Domain ◽  
Protein Protein Interactions ◽  
Carboxy Terminal ◽  
And Function ◽  
Terminal Domains

Abstract The N-terminal domains (NTDs) of many members of the nuclear hormone receptor (NHR) family contain potent transcription-activating functions (AFs). Knowledge of the mechanisms of action of the NTD AFs has lagged, compared with that concerning other important domains of the NHRs. In part, this is because the NTD AFs appear to be unfolded when expressed as recombinant proteins. Recent studies have begun to shed light on the structure and function of the NTD AFs. Recombinant NTD AFs can be made to fold by application of certain osmolytes or when expressed in conjunction with a DNA-binding domain by binding that DNA-binding domain to a DNA response element. The sequence of the DNA binding site may affect the functional state of the AFs domain. If properly folded, NTD AFs can bind certain cofactors and primary transcription factors. Through these, and/or by direct interactions, the NTD AFs may interact with the AF2 domain in the ligand binding, carboxy-terminal portion of the NHRs. We propose models for the folding of the NTD AFs and their protein-protein interactions.

scholarly journals Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482097667
Author(s):  
Ju-Yueh Li ◽  
Chia-Jung Li ◽  
Li-Te Lin ◽  
Kuan-Hao Tsui
Keyword(s):  
Ovarian Cancer ◽  
Protein Interactions ◽  
Malignant Tumors ◽  
Single Gene ◽  
Protein Protein Interactions ◽  
Normal Tissues ◽  
Human Protein Atlas ◽  
Copy Number Changes ◽  
And Function ◽  
Cancer Tissues

Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein–protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the “Human Protein Atlas” database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.

scholarly journals Calculating glycoprotein similarities from mass spectrometric data

2020 ◽  
pp. mcp.R120.002223
Author(s):  
William Edwin Hackett ◽  
Joseph Zaia
Keyword(s):  
Protein Interactions ◽  
Secretory Pathway ◽  
Biological Effects ◽  
Life Forms ◽  
Structure And Function ◽  
Mass Spectrometric ◽  
Protein Quantification ◽  
Protein Glycosylation ◽  
Spectrometric Data ◽  
And Function

Complex protein glycosylation occurs through biosynthetic steps in the secretory pathway that create macro- and microheterogeneity of structure and function.  Required for all life forms, glycosylation diversifies and adapts protein interactions with binding partners that underpin interactions at cell surfaces and pericellular and extracellular environments. Because these biological effects arise from heterogeneity of structure and function, it is necessary to measure their changes as part of the quest to understand nature.  Quite often, however, the assumption behind proteomics that post-translational modifications are discrete additions that can be modeled using the genome as a template does not apply to protein glycosylation.  Rather, it is necessary to quantify the glycosylation distribution at each glycosite and to aggregate this information into a population of mature glycoproteins that exist in a given biological system.  To date, mass spectrometric methods for assigning singly glycosylated peptides are well-established.  But it is necessary to quantify glycosylation heterogeneity accurately in order to gauge the alterations that occur during biological processes.  The task is to quantify the glycosylated peptide forms as accurately as possible and then apply appropriate bioinformatics algorithms to the calculation of micro- and macro-similarities.  In this review, we summarize current approaches for protein quantification as they apply to this glycoprotein similarity problem.

scholarly journals Properties of the phage-shock-protein (Psp) regulatory complex that govern signal transduction and induction of the Psp response in Escherichia coli

Microbiology ◽  
2010 ◽  
Vol 156 (10) ◽  
pp. 2920-2932 ◽  
Author(s):  
Goran Jovanovic ◽  
Christoph Engl ◽  
Antony J. Mayhew ◽  
Patricia C. Burrows ◽  
Martin Buck
Keyword(s):  
Escherichia Coli ◽  
Signal Transduction ◽  
Protein Interactions ◽  
Leucine Zipper ◽  
Negative Control ◽  
Stress Conditions ◽  
Bacterial Cells ◽  
Protein Protein Interactions ◽  
Regulatory Complex ◽  
And Function

The phage-shock-protein (Psp) response maintains the proton-motive force (pmf) under extracytoplasmic stress conditions that impair the inner membrane (IM) in bacterial cells. In Escherichia coli transcription of the pspABCDE and pspG genes requires activation of σ 54-RNA polymerase by the enhancer-binding protein PspF. A regulatory network comprising PspF–A–C–B–ArcB controls psp expression. One key regulatory point is the negative control of PspF imposed by its binding to PspA. It has been proposed that under stress conditions, the IM-bound sensors PspB and PspC receive and transduce the signal(s) to PspA via protein–protein interactions, resulting in the release of the PspA–PspF inhibitory complex and the consequent induction of psp. In this work we demonstrate that PspB self-associates and interacts with PspC via putative IM regions. We present evidence suggesting that PspC has two topologies and that conserved residue G48 and the putative leucine zipper motif are determinants required for PspA interaction and signal transduction upon stress. We also establish that PspC directly interacts with the effector PspG, and show that PspG self-associates. These results are discussed in the context of formation and function of the Psp regulatory complex.

scholarly journals Distinct activities of Scrib module proteins organize epithelial polarity

2020 ◽  
Vol 117 (21) ◽  
pp. 11531-11540 ◽  
Author(s):  
Mark J. Khoury ◽  
David Bilder
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Epithelial Polarity ◽  
Protein Protein Interactions ◽  
Kinase C ◽  
Mutual Antagonism ◽  
Discs Large ◽  
Lethal Giant Larvae ◽  
Epithelial Structure ◽  
And Function

A polarized architecture is central to both epithelial structure and function. In many cells, polarity involves mutual antagonism between the Par complex and the Scribble (Scrib) module. While molecular mechanisms underlying Par-mediated apical determination are well-understood, how Scrib module proteins specify the basolateral domain remains unknown. Here, we demonstrate dependent and independent activities of Scrib, Discs-large (Dlg), and Lethal giant larvae (Lgl) using theDrosophilafollicle epithelium. Our data support a linear hierarchy for localization, but rule out previously proposed protein–protein interactions as essential for polarization. Cortical recruitment of Scrib does not require palmitoylation or polar phospholipid binding but instead an independent cortically stabilizing activity of Dlg. Scrib and Dlg do not directly antagonize atypical protein kinase C (aPKC), but may instead restrict aPKC localization by enabling the aPKC-inhibiting activity of Lgl. Importantly, while Scrib, Dlg, and Lgl are each required, all three together are not sufficient to antagonize the Par complex. Our data demonstrate previously unappreciated diversity of function within the Scrib module and begin to define the elusive molecular functions of Scrib and Dlg.

scholarly journals Structure and function of human Vps20 and Snf7 proteins

2004 ◽  
Vol 377 (3) ◽  
pp. 693-700 ◽  
Author(s):  
Jeremy W. PECK ◽  
Emma T. BOWDEN ◽  
Peter D. BURBELO
Keyword(s):  
Protein Interactions ◽  
Osmotic Shock ◽  
Multivesicular Body ◽  
Protein Protein Interactions ◽  
Interacting Protein ◽  
Endosomal Membrane ◽  
Genomic Studies ◽  
Vacuolar Protein ◽  
And Function

Snf7p (sucrose non-fermenting) and Vps20p (vacuolar protein-sorting) are small coil-coiled proteins involved in yeast MVB (multivesicular body) structure, formation and function. In the present study, we report the identification of three human homologues of yeast Snf7p, designated hSnf7-1, hSnf7-2 and hSnf7-3, and a single human Vps20p homologue, designated hVps20, that may have similar roles in humans. Immunofluorescence studies showed that hSnf7-1 and hSnf7-3 localized in large vesicular structures that also co-localized with late endosomal/lysosomal structures induced by overexpressing an ATPase-defective Vps4-A mutant. In contrast, overexpressed hVps20 showed a typical endosomal membrane-staining pattern, and co-expression of hVps20 with Snf7-1 dispersed the large Snf7-staining vesicles. Interestingly, overexpression of both hSnf7 and hVps20 proteins induced a post-endosomal defect in cholesterol sorting. To explore possible protein–protein interactions involving hSnf7 proteins, we used information from yeast genomic studies showing that yeast Snf7p can interact with proteins involved in MVB function. Using a glutathione S-transferase-capture approach with several mammalian homologues of such yeast Snf7p-interacting proteins, we found that all three hSnf7s interacted with mouse AIP1 [ALG-2 (apoptosis-linked gene 2) interacting protein 1], a mammalian Bro1p [BCK1 (bypass of C kinase)-like resistance to osmotic shock]-containing protein involved in cellular vacuolization and apoptosis. Whereas mapping experiments showed that the N-terminus of AIP1 containing both a Bro1 and an α-helical domain were required for interaction with hSnf7-1, Snf7-1 did not interact with another human Bro1-containing molecule, rhophilin-2. Co-immunoprecipitation experiments confirmed the in vivo interaction of hSnf7-1 and AIP1. Additional immunofluorescence experiments showed that hSnf7-1 recruited cytosolic AIP1 to the Snf7-induced vacuolar-like structures. Together these results suggest that mammalian Vps20, AIP1 and Snf7 proteins, like their yeast counterparts, play roles in MVB function.

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