scholarly journals Activation and assembly of the NADPH oxidase: a structural perspective

2005 ◽  
Vol 386 (3) ◽  
pp. 401-416 ◽  
Author(s):  
Yvonne GROEMPING ◽  
Katrin RITTINGER
Keyword(s):  
Nadph Oxidase ◽  
Protein Interactions ◽  
Structural Information ◽  
Three Dimensional ◽  
Protein Protein Interactions ◽  
Crucial Component ◽  
Enzyme Subunits ◽  
Membrane Components ◽  
Inhibitory Interactions ◽  
Encompassing Model

The NADPH oxidase of professional phagocytes is a crucial component of the innate immune response due to its fundamental role in the production of reactive oxygen species that act as powerful microbicidal agents. The activity of this multi-protein enzyme is dependent on the regulated assembly of the six enzyme subunits at the membrane where oxygen is reduced to superoxide anions. In the resting state, four of the enzyme subunits are maintained in the cytosol, either through auto-inhibitory interactions or through complex formation with accessory proteins that are not part of the active enzyme complex. Multiple inputs are required to disrupt these inhibitory interactions and allow translocation to the membrane and association with the integral membrane components. Protein interaction modules are key regulators of NADPH oxidase assembly, and the protein–protein interactions mediated via these domains have been the target of numerous studies. Many models have been put forward to describe the intricate network of reversible protein interactions that regulate the activity of this enzyme, but an all-encompassing model has so far been elusive. An important step towards an understanding of the molecular basis of NADPH oxidase assembly and activity has been the recent solution of the three-dimensional structures of some of the oxidase components. We will discuss these structures in the present review and attempt to reconcile some of the conflicting models on the basis of the structural information available.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

scholarly journals Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dan Tan ◽  
Qiang Li ◽  
Mei-Jun Zhang ◽  
Chao Liu ◽  
Chengying Ma ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Affinity Purification ◽  
Protein Protein Interactions ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Lysine Residues ◽  
Spacer Arm ◽  
Cross Linker ◽  
Protein Nucleic Acid

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

scholarly journals Protein-protein docking using learned three-dimensional representations

2019 ◽  
Author(s):  
Georgy Derevyanko ◽  
Guillaume Lamoureux
Keyword(s):  
Protein Interactions ◽  
Network Architecture ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Translational Invariance ◽  
Shape Complementarity ◽  
Spatial Features

AbstractProtein-protein interactions are determined by a number of hard-to-capture features related to shape complementarity, electrostatics, and hydrophobicity. These features may be intrinsic to the protein or induced by the presence of a partner. A conventional approach to protein-protein docking consists in engineering a small number of spatial features for each protein, and in minimizing the sum of their correlations with respect to the spatial arrangement of the two proteins. To generalize this approach, we introduce a deep neural network architecture that transforms the raw atomic densities of each protein into complex three-dimensional representations. Each point in the volume containing the protein is described by 48 learned features, which are correlated and combined with the features of a second protein to produce a score dependent on the relative position and orientation of the two proteins. The architecture is based on multiple layers of SE(3)-equivariant convolutional neural networks, which provide built-in rotational and translational invariance of the score with respect to the structure of the complex. The model is trained end-to-end on a set of decoy conformations generated from 851 nonredundant protein-protein complexes and is tested on data from the Protein-Protein Docking Benchmark Version 4.0.

scholarly journals Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells

Structure ◽  
2018 ◽  
Vol 26 (10) ◽  
pp. 1414-1424.e3 ◽  
Author(s):  
Bo Wang ◽  
Zhong-Ru Xie ◽  
Jiawen Chen ◽  
Yinghao Wu
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Protein Protein Interactions ◽  
In Cells

scholarly journals Protein Dynamics in F-like Bacterial Conjugation

Biomedicines ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 362
Author(s):  
Nicholas Bragagnolo ◽  
Christina Rodriguez ◽  
Naveed Samari-Kermani ◽  
Alice Fours ◽  
Mahboubeh Korouzhdehi ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Protein Interactions ◽  
Drug Targets ◽  
Dynamic Models ◽  
Structural Information ◽  
Imaging Techniques ◽  
Antibiotic Resistance Genes ◽  
Protein Protein Interactions ◽  
Secretion Systems ◽  
Type Iv

Efficient in silico development of novel antibiotics requires high-resolution, dynamic models of drug targets. As conjugation is considered the prominent contributor to the spread of antibiotic resistance genes, targeted drug design to disrupt vital components of conjugative systems has been proposed to lessen the proliferation of bacterial antibiotic resistance. Advancements in structural imaging techniques of large macromolecular complexes has accelerated the discovery of novel protein-protein interactions in bacterial type IV secretion systems (T4SS). The known structural information regarding the F-like T4SS components and complexes has been summarized in the following review, revealing a complex network of protein-protein interactions involving domains with varying degrees of disorder. Structural predictions were performed to provide insight on the dynamicity of proteins within the F plasmid conjugative system that lack structural information.

NMR for the design of functional mimetics of protein-protein interactions: one key is in the building of bridges

1998 ◽  
Vol 76 (2-3) ◽  
pp. 177-188 ◽  
Author(s):  
Jianxing Song ◽  
Feng Ni
Keyword(s):  
Protein Interactions ◽  
Binding Sites ◽  
Experimental Approach ◽  
Structural Information ◽  
Peptide Ligands ◽  
Protein Protein Interactions ◽  
Binding Residues ◽  
Related Proteins ◽  
Binding Inhibitors

Using the design of bivalent and bridge-binding inhibitors of thrombin as an example, we review an NMR-based experimental approach for the design of functional mimetics of protein-protein interactions. The strategy includes: (i) identification of binding residues in peptide ligands by differential resonance perturbation, (ii) determination of protein-bound structures of peptide ligands by use of transferred NOEs, (iii) minimization of larger protein and peptide ligands on the basis of NMR structural information, and (iv) linkage of two weakly binding mimetics to produce an inhibitor with enhanced affinity and specificity. This approach can be especially effective for the design of potent and selective functional mimetics of protein-protein interactions because it is less likely that the surfaces of two related proteins or enzymes share two identical binding sites or regions.Key words: NMR, protein-protein interactions, functional mimetics, bridge-binding inhibitors, thrombin.

scholarly journals Inferring interaction partners from protein sequences

2016 ◽  
Author(s):  
Anne-Florence Bitbol ◽  
Robert S. Dwyer ◽  
Lucy J. Colwell ◽  
Ned S. Wingreen
Keyword(s):  
Protein Interactions ◽  
Sequence Data ◽  
Protein Complexes ◽  
A Priori ◽  
Specific Interaction ◽  
Three Dimensional ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Entropy Model ◽  
Interaction Partners

Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners. Hence, the sequences of interacting partners are correlated. Here we exploit these correlations to accurately identify which proteins are specific interaction partners from sequence data alone. Our general approach, which employs a pairwise maximum entropy model to infer direct couplings between residues, has been successfully used to predict the three-dimensional structures of proteins from sequences. Building on this approach, we introduce an iterative algorithm to predict specific interaction partners from among the members of two protein families. We assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. The algorithm proves successful without any a priori knowledge of interaction partners, yielding a striking 0.93 true positive fraction on our complete dataset, and we uncover the origin of this surprising success. Finally, we discuss how our method could be used to predict novel protein-protein interactions.

scholarly journals A Novel Network-Based Algorithm for Predicting Protein-Protein Interactions Using Gene Ontology

2021 ◽  
Vol 12 ◽  
Author(s):  
Lun Hu ◽  
Xiaojuan Wang ◽  
Yu-An Huang ◽  
Pengwei Hu ◽  
Zhu-Hong You
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Structural Information ◽  
Computational Prediction ◽  
Biological Information ◽  
Main Role ◽  
Protein Protein Interactions ◽  
Chronic Infections ◽  
Prediction Algorithms ◽  
Ppi Networks

Proteins are one of most significant components in living organism, and their main role in cells is to undertake various physiological functions by interacting with each other. Thus, the prediction of protein-protein interactions (PPIs) is crucial for understanding the molecular basis of biological processes, such as chronic infections. Given the fact that laboratory-based experiments are normally time-consuming and labor-intensive, computational prediction algorithms have become popular at present. However, few of them could simultaneously consider both the structural information of PPI networks and the biological information of proteins for an improved accuracy. To do so, we assume that the prior information of functional modules is known in advance and then simulate the generative process of a PPI network associated with the biological information of proteins, i.e., Gene Ontology, by using an established Bayesian model. In order to indicate to what extent two proteins are likely to interact with each other, we propose a novel scoring function by combining the membership distributions of proteins with network paths. Experimental results show that our algorithm has a promising performance in terms of several independent metrics when compared with state-of-the-art prediction algorithms, and also reveal that the consideration of modularity in PPI networks provides us an alternative, yet much more flexible, way to accurately predict PPIs.

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