A structural network associated with the kallikrein-kinin and renin-angiotensin systems

2010 ◽  
Vol 391 (4) ◽  
Author(s):  
Veronika Stoka ◽  
Vito Turk
Keyword(s):  
Protein Interactions ◽  
Three Dimensional ◽  
Type I ◽  
Protein Protein Interactions ◽  
Renin Angiotensin ◽  
Protein Protein Interaction ◽  
Protein Interfaces ◽  
Structural Network ◽  
Domain Interactions ◽  
Fibronectin Type

Abstract The kallikrein-kinin and renin-angiotensin (KKS-RAS) systems represent two highly regulated proteolytic systems that are involved in several physiological and pathological processes. Although their protein-protein interactions can be studied using experimental approaches, it is difficult to differentiate between direct physical interactions and functional associations, which do not involve direct atomic contacts between macromolecules. This information can be obtained from an atomic-resolution characterization of the protein interfaces. As a result of this, various three-dimensional-based protein-protein interaction databases have become available. To gain insight into the multilayered interaction of the KKS-RAS systems, we present a protein network that is built up on three-dimensional domain-domain interactions. The essential domains that link these systems are as follows: Cystatin, Peptidase_C1, Thyroglobulin_1, Insulin, CIMR (Cation-independent mannose-6-phosphate receptor repeat), fn2 (Fibronectin type II domain), fn1 (Fibronectin type I domain), EGF, Trypsin, and Serpin. We found that the CIMR domain is located at the core of the network, thus connecting both systems. From the latter, all domain interactors up to level 4 were retrieved, thus displaying a more comprehensive representation of the KKS-RAS structural network.

scholarly journals Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Charlotte Rimbault ◽  
Kashyap Maruthi ◽  
Christelle Breillat ◽  
Camille Genuer ◽  
Sara Crespillo ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Pdz Domain ◽  
Specific Inhibitor ◽  
Rational Approach ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Domain Specific ◽  
Postsynaptic Protein ◽  
Protein Interfaces ◽  
Direct Generation

Abstract Designing highly specific modulators of protein-protein interactions (PPIs) is especially challenging in the context of multiple paralogs and conserved interaction surfaces. In this case, direct generation of selective and competitive inhibitors is hindered by high similarity within the evolutionary-related protein interfaces. We report here a strategy that uses a semi-rational approach to separate the modulator design into two functional parts. We first achieve specificity toward a region outside of the interface by using phage display selection coupled with molecular and cellular validation. Highly selective competition is then generated by appending the more degenerate interaction peptide to contact the target interface. We apply this approach to specifically bind a single PDZ domain within the postsynaptic protein PSD-95 over highly similar PDZ domains in PSD-93, SAP-97 and SAP-102. Our work provides a paralog-selective and domain specific inhibitor of PSD-95, and describes a method to efficiently target other conserved PPI modules.

scholarly journals Dynamic rewiring of the human interactome by interferon signalling

2019 ◽  
Author(s):  
Craig H. Kerr ◽  
Michael A. Skinnider ◽  
Angel M. Madero ◽  
Daniel D.T. Andrews ◽  
R. Greg Stacey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Cell Types ◽  
Antiviral Response ◽  
Type I ◽  
Protein Protein Interactions ◽  
Human Interactome ◽  
Viral Pathogens ◽  
Protein Protein Interaction ◽  
Dependent Protein

ABSTRACTBackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.ResultsWe identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.ConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

2014 ◽  
Vol 4 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Cellular Systems ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Free Distribution ◽  
Protein Protein Interaction ◽  
Domain Interactions

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Domain-Based Prediction and Analysis of Protein-Protein Interactions

2009 ◽  
pp. 29-44 ◽  
Author(s):  
Tatsuya Akutsu ◽  
Morihiro Hayashida
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Interaction Model ◽  
Protein Interaction Data ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Domain Interactions ◽  
Protein Sequence Data

Many methods have been proposed for inference of protein-protein interactions from protein sequence data. This chapter focuses on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This chapter overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, and linear programmingbased method. This chapter also reviews a simple evolutionary model of protein domains, which yields a scalefree distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

Biotechnology ◽  
2019 ◽  
pp. 406-427
Author(s):  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Interaction Model ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Free Distribution ◽  
Protein Protein Interaction ◽  
Domain Interactions ◽  
Protein Sequence Data

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Mutational oncogenic signatures on structurally resolved protein interacting interfaces

2015 ◽  
Author(s):  
Luz Garcia-Alonso ◽  
Joaquin Dopazo
Keyword(s):  
Cancer Patients ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interactions ◽  
Cancer Occurrence ◽  
Domain Interactions ◽  
Cancer Mutations ◽  
Cancer Genome Atlas

The importance of the context of interactions in the proteins mutated in cancer is long known. However, our knowledge on how mutations affecting to protein-protein interactions (PPIs) are related to cancer occurrence and progression is still poor. Here, we extracted the missense somatic mutations from 5920 cancer patients of 33 different cancer types, taken from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), and mapped them onto a structurally resolved interactome, which integrates three-dimensional atomic-level models of domain-domain interactions with experimentally determined PPIs, involving a total of 7580 unique interacting domains that participate in 13160 interactions connecting 4996 proteins. We observed that somatic nonsynonymous mutations tend to concentrate in ordered regions of the affected proteins and, within these, they have a clear preference for the interacting interfaces. Also, we have identified more than 250 interacting interfaces candidate to drive cancer. Examples demonstrate how mutations in the interacting interfaces are strongly associated with patient survival time, while similar mutations in other areas of the same proteins lack this association. Our results suggest that the perturbation caused by cancer mutations in protein interactions is an important factor in explaining the heterogeneity between cancer patients.

scholarly journals A split protease-E. coli ClpXP system quantifies protein–protein interactions in Escherichia coli cells

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Shengchen Wang ◽  
Faying Zhang ◽  
Meng Mei ◽  
Ting Wang ◽  
Yueli Yun ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
High Efficiency ◽  
Cellular Protein ◽  
Type I ◽  
Protein Protein Interactions ◽  
E Coli ◽  
Protein Protein Interaction ◽  
Quantitative Fluorescence ◽  
Temperature Adaptability

AbstractCharacterizing protein–protein interactions (PPIs) is an effective method to help explore protein function. Here, through integrating a newly identified split human Rhinovirus 3 C (HRV 3 C) protease, super-folder GFP (sfGFP), and ClpXP-SsrA protein degradation machinery, we developed a fluorescence-assisted single-cell methodology (split protease-E. coli ClpXP (SPEC)) to explore protein–protein interactions for both eukaryotic and prokaryotic species in E. coli cells. We firstly identified a highly efficient split HRV 3 C protease with high re-assembly ability and then incorporated it into the SPEC method. The SPEC method could convert the cellular protein-protein interaction to quantitative fluorescence signals through a split HRV 3 C protease-mediated proteolytic reaction with high efficiency and broad temperature adaptability. Using SPEC method, we explored the interactions among effectors of representative type I-E and I-F CRISPR/Cas complexes, which combining with subsequent studies of Cas3 mutations conferred further understanding of the functions and structures of CRISPR/Cas complexes.

scholarly journals Gaussia princeps Luciferase as a Reporter for Transcriptional Activity, Protein Secretion, and Protein-Protein Interactions in Salmonella enterica Serovar Typhimurium

2011 ◽  
Vol 78 (1) ◽  
pp. 250-257 ◽  
Author(s):  
Thorsten Wille ◽  
Kathrin Blank ◽  
Christiane Schmidt ◽  
Vivien Vogt ◽  
Roman G. Gerlach
Keyword(s):  
Protein Interactions ◽  
Salmonella Enterica ◽  
Single Copy ◽  
Type I ◽  
Protein Protein Interactions ◽  
Content Type ◽  
Protein Protein Interaction ◽  
Serovar Typhimurium ◽  
Secretion Chaperone ◽  
Gaussia Princeps

ABSTRACTGaussia princepsluciferase (Gluc) is widely used as a reporter in eukaryotes, but data about its applicability in bacteria are very limited. Here we show that a codon-optimized Gluc gene can be efficiently expressed inSalmonella entericaserovar Typhimurium. To test different Gluc variants as transcriptional reporters, we used thesiiApromoter ofSalmonellapathogenicity island 4 (SPI-4) driving expression of either an episomal or a chromosomally integrated Gluc gene. Most reliable results were obtained from lysates of single-copy Gluc reporter strains. Given the small size, high activity, and cofactor independence of Gluc, it might be especially suited to monitor secretion of bacterial proteins. We demonstrate its usefulness by luminescence detection of fusion proteins of Gluc and C-terminal portions of the SPI-4-encoded, type I-secreted adhesin SiiE in supernatants. The SiiE C-terminal moiety including immunoglobulin (Ig) domain 53 is essential and sufficient for mediating type I-dependent secretion of Gluc. In eukaryotes, protein-protein interaction studies based on split-Gluc protein complementation assays (PCA) could be established. We adapted these methods for use inSalmonella, demonstrating the interaction between the SPI-1-encoded effector SipA and its cognate secretion chaperone InvB. In conclusion, the versatile Gluc can be used to address a variety of biological questions, thus representing a valuable addition to the toolbox of modern molecular biology and microbiology.

scholarly journals Sequence co-evolution gives 3D contacts and structures of protein complexes

2014 ◽  
Author(s):  
Thomas A. Hopf ◽  
Charlotta P.I. Schärfe ◽  
João P.G.L.M. Rodrigues ◽  
Anna G. Green ◽  
Chris Sander ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
3D Structure ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Evolutionary Sequence ◽  
Protein Protein Interactions ◽  
Interacting Protein ◽  
Protein Protein Interaction ◽  
Unknown Structure

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

Study of Protein-Protein Interactions from Multiple Data Sources

2008 ◽  
pp. 280-307
Author(s):  
Tu Bao Ho ◽  
Thanh Phuong Nguyen ◽  
Tuan Nam Tran
Keyword(s):  
Protein Interactions ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Data Sources ◽  
Protein Protein Interactions ◽  
Prediction Rules ◽  
Multiple Data Sources ◽  
Protein Protein Interaction ◽  
Multiple Data ◽  
Domain Interactions

The objective of this paper is twofold. First is to provide a survey of computational methods for protein-protein interaction (PPI) study. Second is to introduce our work and results in using inductive logic programming to learn prediction rules for PPI and DDI (domain-domain interactions) from multiple data sources. We show advantages of ex-ploiting various types of data in these important problems of bioinformatics.

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