scholarly journals In Vivo Methods to Study Protein–Protein Interactions as Key Players in Mycobacterium Tuberculosis Virulence

Pathogens ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 173 ◽  
Author(s):  
Veyron-Churlet ◽  
Locht
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Unknown Protein ◽  
Protein Functions ◽  
Transient Interactions ◽  
Protein Complementation

Studies on protein–protein interactions (PPI) can be helpful for the annotation of unknown protein functions and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher tuberculosis (TB) pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

scholarly journals In Vivo Methods to Study Protein-Protein Interactions as Key Players in Mycobacterium tuberculosis Virulence

2019 ◽  
Author(s):  
Romain Veyron-Churlet ◽  
Camille Locht
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Protein Interactions ◽  
Protein Function ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Unknown Protein ◽  
Transient Interactions ◽  
Protein Complementation

Studies on Protein-Protein interactions (PPI) can be helpful for the annotation of unknown protein function and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher TB pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

scholarly journals Assay Systems for Profiling Deubiquitinating Activity

2020 ◽  
Vol 21 (16) ◽  
pp. 5638
Author(s):  
Jinhong Cho ◽  
Jinyoung Park ◽  
Eunice EunKyeong Kim ◽  
Eun Joo Song
Keyword(s):  
Protein Degradation ◽  
Protein Interactions ◽  
Live Cells ◽  
Deubiquitinating Enzyme ◽  
Protein Protein Interactions ◽  
Deubiquitinating Enzymes ◽  
Cell Lysates ◽  
Cellular Processes

Deubiquitinating enzymes regulate various cellular processes, particularly protein degradation, localization, and protein–protein interactions. The dysregulation of deubiquitinating enzyme (DUB) activity has been linked to several diseases; however, the function of many DUBs has not been identified. Therefore, the development of methods to assess DUB activity is important to identify novel DUBs, characterize DUB selectivity, and profile dynamic DUB substrates. Here, we review various methods of evaluating DUB activity using cell lysates or purified DUBs, as well as the types of probes used in these methods. In addition, we introduce some techniques that can deliver DUB probes into the cells and cell-permeable activity-based probes to directly visualize and quantify DUB activity in live cells. This review could contribute to the development of DUB inhibitors by providing important information on the characteristics and applications of various probes used to evaluate and detect DUB activity in vitro and in vivo.

scholarly journals Luminescence- and Fluorescence-Based Complementation Assays to Screen for GPCR Oligomerization: Current State of the Art

2019 ◽  
Vol 20 (12) ◽  
pp. 2958 ◽  
Author(s):  
Wouters ◽  
Vasudevan ◽  
Crans ◽  
Saini ◽  
Stove
Keyword(s):  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Vitro Assay ◽  
Current State ◽  
Target Disease ◽  
Gpcr Dimer ◽  
Overexpression System ◽  
Protein Complementation

G protein-coupled receptors (GPCRs) have the propensity to form homo- and heterodimers. Dysfunction of these dimers has been associated with multiple diseases, e.g., pre-eclampsia, schizophrenia, and depression, among others. Over the past two decades, considerable efforts have been made towards the development of screening assays for studying these GPCR dimer complexes in living cells. As a first step, a robust in vitro assay in an overexpression system is essential to identify and characterize specific GPCR–GPCR interactions, followed by methodologies to demonstrate association at endogenous levels and eventually in vivo. This review focuses on protein complementation assays (PCAs) which have been utilized to study GPCR oligomerization. These approaches are typically fluorescence- and luminescence-based, making identification and localization of protein–protein interactions feasible. The GPCRs of interest are fused to complementary fluorescent or luminescent fragments that, upon GPCR di- or oligomerization, may reconstitute to a functional reporter, of which the activity can be measured. Various protein complementation assays have the disadvantage that the interaction between the reconstituted split fragments is irreversible, which can lead to false positive read-outs. Reversible systems offer several advantages, as they do not only allow to follow the kinetics of GPCR–GPCR interactions, but also allow evaluation of receptor complex modulation by ligands (either agonists or antagonists). Protein complementation assays may be used for high throughput screenings as well, which is highly relevant given the growing interest and effort to identify small molecule drugs that could potentially target disease-relevant dimers. In addition to providing an overview on how PCAs have allowed to gain better insights into GPCR–GPCR interactions, this review also aims at providing practical guidance on how to perform PCA-based assays.

scholarly journals Application of a bacterial two-hybrid system for the analysis of protein–protein interactions between FemABX family proteins

Microbiology ◽  
2003 ◽  
Vol 149 (10) ◽  
pp. 2733-2738 ◽  
Author(s):  
Susanne Rohrer ◽  
Brigitte Berger-Bächi
Keyword(s):  
Hybrid System ◽  
Protein Interactions ◽  
Gel Filtration ◽  
Protein Protein Interactions ◽  
Peptidoglycan Biosynthesis ◽  
Cellular Processes ◽  
Gram Positive Bacteria ◽  
Pulldown Assay ◽  
Two Hybrid

Protein–protein interactions play an important role in all cellular processes. The development of two-hybrid systems in yeast and bacteria allows for in vivo assessment of such interactions. Using a recently developed bacterial two-hybrid system, the interactions of the Staphylococcus aureus proteins FemA, FemB and FmhB, members of the FemABX protein family, which is involved in peptidoglycan biosynthesis and β-lactam resistance of numerous Gram-positive bacteria, were analysed. While FmhB is involved in the addition of glycine 1 of the pentaglycine interpeptide of S. aureus peptidoglycan, FemA and FemB are specific for glycines 2/3 and 4/5, respectively. FemA–FemA, FemA–FemB and FemB–FemB interactions were found, while FmhB exists solely as a monomer. Interactions detected by the bacterial two-hybrid system were confirmed using the glutathione S-transferase-pulldown assay and gel filtration.

Applications of Mass Spectrometry in Proteomics

2013 ◽  
Vol 66 (7) ◽  
pp. 721 ◽  
Author(s):  
Izabela Sokolowska ◽  
Armand G. Ngounou Wetie ◽  
Alisa G. Woods ◽  
Costel C. Darie
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Identification ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Cellular Processes ◽  
Physiological Processes ◽  
Protein Functions ◽  
Instruments And Techniques ◽  
Accurate Mass Spectrometry

Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

scholarly journals Label-free methods for optical in vitro characterization of protein–protein interactions

2021 ◽  
Author(s):  
Fabian Soltermann ◽  
Weston B. Struwe ◽  
Philipp Kukura
Keyword(s):  
Protein Interactions ◽  
Label Free ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
P Protein ◽  
In Vitro Characterization ◽  
And Function

Protein–protein interactions are involved in the regulation and function of the majority of cellular processes.

scholarly journals Structural and functional characterization of Rpn12 identifies residues required for Rpn10 proteasome incorporation

2012 ◽  
Vol 448 (1) ◽  
pp. 55-65 ◽  
Author(s):  
Jonas Boehringer ◽  
Christiane Riedinger ◽  
Konstantinos Paraskevopoulos ◽  
Eachan O. D. Johnson ◽  
Edward D. Lowe ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Functional Characterization ◽  
Ubiquitin Proteasome System ◽  
26S Proteasome ◽  
Structural Motif ◽  
Protein Protein Interactions ◽  
Ubiquitin Proteasome

The ubiquitin–proteasome system targets selected proteins for degradation by the 26S proteasome. Rpn12 is an essential component of the 19S regulatory particle and plays a role in recruiting the extrinsic ubiquitin receptor Rpn10. In the present paper we report the crystal structure of Rpn12, a proteasomal PCI-domain-containing protein. The structure helps to define a core structural motif for the PCI domain and identifies potential sites through which Rpn12 might form protein–protein interactions. We demonstrate that mutating residues at one of these sites impairs Rpn12 binding to Rpn10 in vitro and reduces Rpn10 incorporation into proteasomes in vivo.

Computational Methods for Prediction of Protein-Protein Interactions

2017 ◽  
pp. 309-340
Author(s):  
Sneha Rai ◽  
Sonika Bhatnagar
Keyword(s):  
Network Topology ◽  
Protein Interactions ◽  
Biological Significance ◽  
Experimental Methods ◽  
Ease Of Use ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Large Numbers ◽  
Transient Interactions ◽  
Ppi Prediction

The key signaling pathways in cellular processes involve protein-protein interactions (PPIs). A perturbation in the balance of PPIs occurs in various pathophysiological processes. There are a large numbers of experimental methods for detection of PPIs. However, experimental PPI determination is time consuming, expensive, error prone and does not effectively cover transient interactions. Therefore, overlaying and integration of predictive methods with experimental results provides statistical robustness and biological significance to the PPI data. In this chapter, the authors describe PPIs in terms of types, importance, and biological consequences. This chapter also provides a comprehensive description on various computational approaches for PPI prediction. Prediction of PPI can be done through: 1) Genomic information based methods 2) Structure based methods 3) Network topology based methods: 4) Literature and data mining based methods 5) Machine learning methods. For ease of use and convenience, a summary of various databases and software for PPI prediction has been provided.

scholarly journals Characterization of known protein complexes using k-connectivity and other topological measures

F1000Research ◽  
2015 ◽  
Vol 2 ◽  
pp. 172
Author(s):  
Suzanne R Gallagher ◽  
Debra S Goldberg
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Coefficient ◽  
Edge Density ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Unknown Protein ◽  
Topological Measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

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