Bradyrhizobium diazoefficiens USDA 110-Glycine max interactome provides candidate proteins associated with symbiosis

Mapping Intimacies ◽

10.1101/288811 ◽

2018 ◽

Author(s):

Li Zhang ◽

Jin-Yang Liu ◽

Huan Gu ◽

Yanfang Du ◽

Jian-Fang Zuo ◽

...

Keyword(s):

Glycine Max ◽

Protein Interactions ◽

Interaction Network ◽

Snare Proteins ◽

Protein Protein Interactions ◽

Bacterial Proteins ◽

Dicarboxylate Transport ◽

Protein Interactome ◽

Subnetwork Analysis ◽

Shock Proteins

AbstractAlthough the legume-rhizobium symbiosis is a most important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog and domain-based approaches, we constructed an inter-species protein interactome with 5115 protein-protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was validated by expression pattern analysis in nodules, GO term semantic similarity, and co-expression analysis. One sub-network was further confirmed using luciferase complementation image assay. In the G. max-B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top ten highly interacting proteins (hubs) for each of the two species. KEGG pathway analysis for each hub showed that two 14-3-3 proteins (SGF14g and SGF14k) and five heat shock proteins in G. max are possibly involved in symbiosis, and ten hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related SNARE proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome and symbiosis proteins provide a valuable basis for understanding the molecular mechanism of root nodule symbiosis in soybean.

The Protein Interactome of Mycobacteriophage Giles Predicts Functions for Unknown Proteins

Journal of Bacteriology ◽

10.1128/jb.00164-15 ◽

2015 ◽

Vol 197 (15) ◽

pp. 2508-2516 ◽

Cited By ~ 11

Author(s):

Jitender Mehla ◽

Rebekah M. Dedrick ◽

J. Harry Caufield ◽

Rachel Siefring ◽

Megan Mair ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Protein Interactions ◽

Interaction Network ◽

Protein Protein Interactions ◽

High Confidence ◽

Content Type ◽

Protein Protein Interaction ◽

Protein Interactome ◽

Therapeutic Tools ◽

Genetic Novelty

ABSTRACTMycobacteriophages are viruses that infect mycobacterial hosts and are prevalent in the environment. Nearly 700 mycobacteriophage genomes have been completely sequenced, revealing considerable diversity and genetic novelty. Here, we have determined the protein complement of mycobacteriophage Giles by mass spectrometry and mapped its genome-wide protein interactome to help elucidate the roles of its 77 predicted proteins, 50% of which have no known function. About 22,000 individual yeast two-hybrid (Y2H) tests with four different Y2H vectors, followed by filtering and retest screens, resulted in 324 reproducible protein-protein interactions, including 171 (136 nonredundant) high-confidence interactions. The complete set of high-confidence interactions among Giles proteins reveals new mechanistic details and predicts functions for unknown proteins. The Giles interactome is the first for any mycobacteriophage and one of just five known phage interactomes so far. Our results will help in understanding mycobacteriophage biology and aid in development of new genetic and therapeutic tools to understandMycobacterium tuberculosis.IMPORTANCEMycobacterium tuberculosiscauses over 9 million new cases of tuberculosis each year. Mycobacteriophages, viruses of mycobacterial hosts, hold considerable potential to understand phage diversity, evolution, and mycobacterial biology, aiding in the development of therapeutic tools to control mycobacterial infections. The mycobacteriophage Giles protein-protein interaction network allows us to predict functions for unknown proteins and shed light on major biological processes in phage biology. For example, Giles gp76, a protein of unknown function, is found to associate with phage packaging and maturation. The functions of mycobacteriophage-derived proteins may suggest novel therapeutic approaches for tuberculosis. Our ORFeome clone set of Giles proteins and the interactome data will be useful resources for phage interactomics.

Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

Frequent assembly of chimeric complexes in the protein interaction network of an interspecies yeast hybrid

Molecular Biology and Evolution ◽

10.1093/molbev/msaa298 ◽

2020 ◽

Author(s):

Rohan Dandage ◽

Caroline M Berger ◽

Isabelle Gagnon-Arsenault ◽

Kyung-Mee Moon ◽

Richard Greg Stacey ◽

...

Keyword(s):

Protein Interactions ◽

Molecular Level ◽

Yeast Species ◽

Protein Complexes ◽

Mitochondrial Protein ◽

Chimeric Protein ◽

Interaction Network ◽

Protein Protein Interactions ◽

Extreme Phenotypes ◽

Yeast Hybrid

Abstract Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein-protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein-protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein-protein interactions in the hybrid, for instance in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteins.

The Protein Interactome of Glycolysis in Escherichia coli

Proteomes ◽

10.3390/proteomes9020016 ◽

2021 ◽

Vol 9 (2) ◽

pp. 16

Author(s):

Shomeek Chowdhury ◽

Stephen Hepper ◽

Mudassir K. Lodi ◽

Milton H. Saier ◽

Peter Uetz

Keyword(s):

Escherichia Coli ◽

Protein Interactions ◽

Allosteric Regulation ◽

Glycolytic Enzymes ◽

Protein Protein Interactions ◽

Post Translational Modification ◽

Interacting Protein ◽

E Coli ◽

Protein Interactome ◽

The Impact

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data

BioMed Research International ◽

10.1155/2016/2891918 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13

Author(s):

Stefan Kalkhof ◽

Stefan Schildbach ◽

Conny Blumert ◽

Friedemann Horn ◽

Martin von Bergen ◽

...

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Isotope Labeling ◽

Affinity Purification ◽

Interaction Network ◽

Mass Spectrometry Data ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Binding Behavior ◽

Bona Fide

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

TC2N: A Novel Vital Oncogene or Tumor Suppressor Gene In Cancers

Frontiers in Immunology ◽

10.3389/fimmu.2021.764749 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hanyang Li ◽

He Fang ◽

Li Chang ◽

Shuang Qiu ◽

Xiaojun Ren ◽

...

Keyword(s):

Tumor Suppressor ◽

Tumor Suppressor Gene ◽

Protein Interactions ◽

Malignant Tumors ◽

Suppressor Gene ◽

C2 Domain ◽

Protein Protein Interactions ◽

C2 Domains ◽

Different Types ◽

Shock Proteins

Several C2 domain-containing proteins play key roles in tumorigenesis, signal transduction, and mediating protein–protein interactions. Tandem C2 domains nuclear protein (TC2N) is a tandem C2 domain-containing protein that is differentially expressed in several types of cancers and is closely associated with tumorigenesis and tumor progression. Notably, TC2N has been identified as an oncogene in lung and gastric cancer but as a tumor suppressor gene in breast cancer. Recently, a large number of tumor-associated antigens (TAAs), such as heat shock proteins, alpha-fetoprotein, and carcinoembryonic antigen, have been identified in a variety of malignant tumors. Differences in the expression levels of TAAs between cancer cells and normal cells have led to these antigens being investigated as diagnostic and prognostic biomarkers and as novel targets in cancer treatment. In this review, we summarize the clinical characteristics of TC2N-positive cancers and potential mechanisms of action of TC2N in the occurrence and development of specific cancers. This article provides an exploration of TC2N as a potential target for the diagnosis and treatment of different types of cancers.

Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

Computational Knowledge Discovery for Bioinformatics Research ◽

10.4018/978-1-4666-1785-8.ch013 ◽

2013 ◽

pp. 222-238

Author(s):

Young-Rae Cho ◽

Aidong Zhang

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Functional Characterization ◽

Flow Simulation ◽

Protein Protein Interactions ◽

Systematic Analysis ◽

Graph Theoretic ◽

Interactome Network ◽

Protein Interactome ◽

Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1918068117 ◽

2020 ◽

Vol 117 (21) ◽

pp. 11836-11842 ◽

Cited By ~ 1

Author(s):

Shayne D. Wierbowski ◽

Tommy V. Vo ◽

Pascal Falter-Braun ◽

Timothy O. Jobe ◽

Lars H. Kruse ◽

...

Keyword(s):

Protein Interactions ◽

Massively Parallel ◽

Model Organisms ◽

Protein Protein Interactions ◽

Numerous Model ◽

High Quality Protein ◽

Protein Interactome ◽

Wide Range ◽

Dna Elements ◽

General Tool

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we developPCR-mediatedLinkage of barcodedAdaptersTo nucleic acidElements forsequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome forOryza sativacovering 2,300 genes and constructing a high-quality protein–protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein–protein interactions in a wide range of organisms.

Conserved Central Intraviral Protein Interactome of the Herpesviridae Family

mSystems ◽

10.1128/msystems.00295-19 ◽

2019 ◽

Vol 4 (5) ◽

Cited By ~ 1

Author(s):

Anna Hernández Durán ◽

Kay Grünewald ◽

Maya Topf

Keyword(s):

Protein Interactions ◽

Computational Models ◽

Functional Organization ◽

Driving Forces ◽

Protein Complexes ◽

Structural Features ◽

System Level ◽

Protein Protein Interactions ◽

Protein Interactome ◽

Essential Components

ABSTRACT Protein interactions are major driving forces behind the functional phenotypes of biological processes. As such, evolutionary footprints are reflected in system-level collections of protein-protein interactions (PPIs), i.e., protein interactomes. We conducted a comparative analysis of intraviral protein interactomes for representative species of each of the three subfamilies of herpesviruses (herpes simplex virus 1, human cytomegalovirus, and Epstein-Barr virus), which are highly prevalent etiologic agents of important human diseases. The intraviral interactomes were reconstructed by combining experimentally supported and computationally predicted protein-protein interactions. Using cross-species network comparison, we then identified family-wise conserved interactions and protein complexes, which we defined as a herpesviral “central” intraviral protein interactome. A large number of widely accepted conserved herpesviral protein complexes are present in this central intraviral interactome, encouragingly supporting the biological coherence of our results. Importantly, these protein complexes represent most, if not all, of the essential steps required during a productive life cycle. Hence the central intraviral protein interactome could plausibly represent a minimal infectious interactome of the herpesvirus family across a variety of hosts. Our data, which have been integrated into our herpesvirus interactomics database, HVint2.0, could assist in creating comprehensive system-level computational models of this viral lineage. IMPORTANCE Herpesviruses are an important socioeconomic burden for both humans and livestock. Throughout their long evolutionary history, individual herpesvirus species have developed remarkable host specificity, while collectively the Herpesviridae family has evolved to infect a large variety of eukaryotic hosts. The development of approaches to fight herpesvirus infections has been hampered by the complexity of herpesviruses’ genomes, proteomes, and structural features. The data and insights generated by our study add to the understanding of the functional organization of herpesvirus-encoded proteins, specifically of family-wise conserved features defining essential components required for a productive infectious cycle across different hosts, which can contribute toward the conceptualization of antiherpetic infection strategies with an effect on a broader range of target species. All of the generated data have been made freely available through our HVint2.0 database, a dedicated resource of curated herpesvirus interactomics purposely created to promote and assist future studies in the field.

mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

Nucleic Acids Research ◽

10.1093/nar/gkz383 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W338-W344 ◽

Cited By ~ 51

Author(s):

Carlos H M Rodrigues ◽

Yoochan Myung ◽

Douglas E V Pires ◽

David B Ascher

Keyword(s):

Protein Interactions ◽

Interaction Network ◽

Evolutionary Information ◽

Biological Processes ◽

Missense Mutations ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Residue Interaction ◽

User Friendly ◽

Network Metrics

AbstractProtein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.