scholarly journals Hydrophobic Mismatch Controls the Mode of Membrane-Mediated Interactions of Transmembrane Peptides

Membranes ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Oleg V. Kondrashov ◽  
Peter I. Kuzmin ◽  
Sergey A. Akimov
Keyword(s):  
Long Range ◽  
Protein Interactions ◽  
Lipid Membranes ◽  
Theory Of Elasticity ◽  
Membrane Thickness ◽  
Protein Protein Interactions ◽  
Elastic Deformations ◽  
Transmembrane Peptides ◽  
Cooperative Action ◽  
Cellular Processes

Various cellular processes require the concerted cooperative action of proteins. The possibility for such synchronization implies the occurrence of specific long-range interactions between the involved protein participants. Bilayer lipid membranes can mediate protein–protein interactions via relatively long-range elastic deformations induced by the incorporated proteins. We considered the interactions between transmembrane peptides mediated by elastic deformations using the framework of the theory of elasticity of lipid membranes. An effective peptide shape was assumed to be cylindrical, hourglass-like, or barrel-like. The interaction potentials were obtained for membranes of different thicknesses and elastic rigidities. Cylindrically shaped peptides manifest almost neutral average interactions—they attract each other at short distances and repel at large ones, independently of membrane thickness or rigidity. The hourglass-like peptides repel each other in thin bilayers and strongly attract each other in thicker bilayers. On the contrary, the barrel-like peptides repel each other in thick bilayers and attract each other in thinner membranes. These results potentially provide possible mechanisms of control for the mode of protein–protein interactions in membrane domains with different bilayer thicknesses.

Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

2018 ◽  
Vol 25 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Ylenia Cau ◽  
Daniela Valensin ◽  
Mattia Mori ◽  
Sara Draghi ◽  
Maurizio Botta
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Physiological Role ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Partners ◽  
High Sequence Homology ◽  
Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

Visual detection of binary, ternary, and quaternary protein interactions in fission yeast by Pil1 co-tethering assay

2021 ◽  
Author(s):  
Zhong-Qiu Yu ◽  
Xiao-Man Liu ◽  
Dan Zhao ◽  
Dan-Dan Xu ◽  
Li-Lin Du
Keyword(s):  
Fission Yeast ◽  
Protein Interactions ◽  
Visual Detection ◽  
Protein Protein Interactions ◽  
Phosphatidylinositol 3 Kinase ◽  
Basic Form ◽  
Bait Protein ◽  
Cellular Processes ◽  
Prey Protein ◽  
Versatile Tool

Protein-protein interactions are vital for executing nearly all cellular processes. To facilitate the detection of protein-protein interactions in living cells of the fission yeast Schizosaccharomyces pombe, here we present an efficient and convenient method termed the Pil1 co-tethering assay. In its basic form, we tether a bait protein to mCherry-tagged Pil1, which forms cortical filamentary structures, and examine whether a GFP-tagged prey protein colocalizes with the bait. We demonstrate that this assay is capable of detecting pairwise protein-protein interactions of cytosolic proteins and nuclear proteins. Furthermore, we show that this assay can be used for detecting not only binary protein-protein interactions, but also ternary and quaternary protein-protein interactions. Using this assay, we systematically characterized the protein-protein interactions in the Atg1 complex and in the phosphatidylinositol 3-kinase (PtdIns3K) complexes and found that Atg38 is incorporated into the PtdIns3K complex I via an Atg38-Vps34 interaction. Our data show that this assay is a useful and versatile tool and should be added to the routine toolbox of fission yeast researchers.

scholarly journals Protein–protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics

2018 ◽  
Vol 14 ◽  
pp. 2881-2896 ◽  
Author(s):  
Laura Carro
Keyword(s):  
Protein Interactions ◽  
Bacterial Infections ◽  
Multidrug Resistant ◽  
Resistant Bacteria ◽  
Protein Protein Interactions ◽  
Lead Discovery ◽  
Antibiotic Development ◽  
Cellular Processes ◽  
Ppi Networks ◽  
New Antibiotics

Antibiotics are potent pharmacological weapons against bacterial infections; however, the growing antibiotic resistance of microorganisms is compromising the efficacy of the currently available pharmacotherapies. Even though antimicrobial resistance is not a new problem, antibiotic development has failed to match the growth of resistant pathogens and hence, it is highly critical to discover new anti-infective drugs with novel mechanisms of action which will help reducing the burden of multidrug-resistant microorganisms. Protein–protein interactions (PPIs) are involved in a myriad of vital cellular processes and have become an attractive target to treat diseases. Therefore, targeting PPI networks in bacteria may offer a new and unconventional point of intervention to develop novel anti-infective drugs which can combat the ever-increasing rate of multidrug-resistant bacteria. This review describes the progress achieved towards the discovery of molecules that disrupt PPI systems in bacteria for which inhibitors have been identified and whose targets could represent an alternative lead discovery strategy to obtain new anti-infective molecules.

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.

Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation

2019 ◽  
Vol 21 (5) ◽  
pp. 1798-1805 ◽  
Author(s):  
Kai Yu ◽  
Qingfeng Zhang ◽  
Zekun Liu ◽  
Yimeng Du ◽  
Xinjiao Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Web Server ◽  
Data Sets ◽  
Lysine Acetylation ◽  
Protein Acetylation ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Lysine Acetylation ◽  
Specific Regulation

Abstract Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein–protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.

scholarly journals Systematic identification of signal integration by protein kinase A

2015 ◽  
Vol 112 (14) ◽  
pp. 4501-4506 ◽  
Author(s):  
Marie Filteau ◽  
Guillaume Diss ◽  
Francisco Torres-Quiroz ◽  
Alexandre K. Dubé ◽  
Andrea Schraffl ◽  
...  
Keyword(s):  
Protein Kinase ◽  
Protein Kinase A ◽  
Protein Interactions ◽  
Carbon Sources ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Complex Formation ◽  
Growth And Aging ◽  
Kinase A

Cellular processes and homeostasis control in eukaryotic cells is achieved by the action of regulatory proteins such as protein kinase A (PKA). Although the outbound signals from PKA directed to processes such as metabolism, growth, and aging have been well charted, what regulates this conserved regulator remains to be systematically identified to understand how it coordinates biological processes. Using a yeast PKA reporter assay, we identified genes that influence PKA activity by measuring protein–protein interactions between the regulatory and the two catalytic subunits of the PKA complex in 3,726 yeast genetic-deletion backgrounds grown on two carbon sources. Overall, nearly 500 genes were found to be connected directly or indirectly to PKA regulation, including 80 core regulators, denoting a wide diversity of signals regulating PKA, within and beyond the described upstream linear pathways. PKA regulators span multiple processes, including the antagonistic autophagy and methionine biosynthesis pathways. Our results converge toward mechanisms of PKA posttranslational regulation by lysine acetylation, which is conserved between yeast and humans and that, we show, regulates protein complex formation in mammals and carbohydrate storage and aging in yeast. Taken together, these results show that the extent of PKA input matches with its output, because this kinase receives information from upstream and downstream processes, and highlight how biological processes are interconnected and coordinated by PKA.

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.

scholarly journals Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

2012 ◽  
Vol 2012 ◽  
pp. 1-23
Author(s):  
J. M. Urquiza ◽  
I. Rojas ◽  
H. Pomares ◽  
J. Herrera ◽  
J. P. Florido ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hierarchical Clustering ◽  
Protein Interactions ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Machine Model ◽  
Cellular Processes ◽  
Parallel Feature ◽  
Ppi Prediction

Protein-protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets.

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).

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