Inferring network mechanisms: the Drosophila melanogaster protein interaction network.
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ABSTRACT:
SUBMITTER: Middendorf M
PROVIDER: S-EPMC552930 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Most of the known Drosophila architectural proteins interact with an important cofactor, CP190, that contains three domains (BTB, M, and D) that are involved in protein-protein interactions. The highly conserved N-terminal CP190 BTB domain forms a stable homodimer that interacts with unstructured regions in the three best-characterized architectural proteins: dCTCF, Su(Hw), and Pita. Here, we identified two new CP190 partners, CG4730 and CG31365, that interact with the BTB domain. The CP190 BTB resembles the previously characterized human BCL6 BTB domain, which uses its hydrophobic groove to specifically associate with unstructured regions of several transcriptional repressors. Using GST pull-down and yeast two-hybrid assays, we demonstrated that mutations in the hydrophobic groove strongly affect the affinity of CP190 BTB for the architectural proteins. In the yeast two-hybrid assay, we found that architectural proteins use various mechanisms to improve the efficiency of interaction with CP190. Pita and Su(Hw) have two unstructured regions that appear to simultaneously interact with hydrophobic grooves in the BTB dimer. In dCTCF and CG31365, two adjacent regions interact simultaneously with the hydrophobic groove of the BTB and the M domain of CP190. Finally, CG4730 interacts with the BTB, M, and D domains of CP190 simultaneously. These results suggest that architectural proteins use different mechanisms to increase the efficiency of interaction with CP190.
| S-EPMC8618245 | biostudies-literature
Project description:Determining the composition of protein complexes is an essential step toward understanding the cell as an integrated system. Using coaffinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly 5,000 individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a statistical framework designed to define individual protein-protein interactions, led to the generation of a Drosophila protein interaction map (DPiM) encompassing 556 protein complexes. The high quality of the DPiM and its usefulness as a paradigm for metazoan proteomes are apparent from the recovery of many known complexes, significant enrichment for shared functional attributes, and validation in human cells. The DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. The DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution.
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Project description:Receptor for Activated protein C kinase 1 (RACK1) is a scaffold protein that has been found in association with several signaling complexes, and with the 40S subunit of the ribosome. Using the model organism Drosophila melanogaster, we recently showed that RACK1 is required at the ribosome for internal ribosome entry site (IRES)-mediated translation of viruses. Here, we report a proteomic characterization of the interactome of RACK1 in Drosophila S2 cells. We carried out Label-Free quantitation using both Data-Dependent and Data-Independent Acquisition (DDA and DIA, respectively) and observed a significant advantage for the Sequential Window Acquisition of all THeoretical fragment-ion spectra (SWATH) method, both in terms of identification of interactants and quantification of low abundance proteins. These data represent the first SWATH spectral library available for Drosophila and will be a useful resource for the community. A total of 52 interacting proteins were identified, including several molecules involved in translation such as structural components of the ribosome, factors regulating translation initiation or elongation, and RNA binding proteins. Among these 52 proteins, 15 were identified as partners by the SWATH strategy only. Interestingly, these 15 proteins are significantly enriched for the functions translation and nucleic acid binding. This enrichment reflects the engagement of RACK1 at the ribosome and highlights the added value of SWATH analysis. A functional screen did not reveal any protein sharing the interesting properties of RACK1, which is required for IRES-dependent translation and not essential for cell viability. Intriguingly however, 10 of the RACK1 partners identified restrict replication of Cricket paralysis virus (CrPV), an IRES-containing virus.
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Project description:Extensive studies on tumor suppressor genes (TSGs) are helpful to understand the pathogenesis of cancer and design effective treatments. However, identifying TSGs using traditional experiments is quite difficult and time consuming. Developing computational methods to identify possible TSGs is an alternative way. In this study, we proposed two computational methods that integrated two network diffusion algorithms, including Laplacian heat diffusion (LHD) and random walk with restart (RWR), to search possible genes in the whole network. These two computational methods were LHD-based and RWR-based methods. To increase the reliability of the putative genes, three strict screening tests followed to filter genes obtained by these two algorithms. After comparing the putative genes obtained by the two methods, we designated twelve genes (e.g., MAP3K10, RND1, and OTX2) as common genes, 29 genes (e.g., RFC2 and GUCY2F) as genes that were identified only by the LHD-based method, and 128 genes (e.g., SNAI2 and FGF4) as genes that were inferred only by the RWR-based method. Some obtained genes can be confirmed as novel TSGs according to recent publications, suggesting the utility of our two proposed methods. In addition, the reported genes in this study were quite different from those reported in a previous one.
| S-EPMC6068090 | biostudies-literature
Project description:BACKGROUND: Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. RESULTS: In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. CONCLUSION: The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains.
| S-EPMC4029543 | biostudies-literature
Project description:Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data.
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Project description:BackgroundProtein-protein interactions in cells are widely explored using small-scale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge. We present "4N", a novel method for detecting protein complexes in such data. Our method is a heuristic algorithm based on Near Neighbor Network (3N) clustering. It is written in R, it is faster than model-based methods, and has only a small number of tuning parameters. We explain the application of our new method to real immunoprecipitation results and two artificial datasets. We show that the method can infer protein complexes from protein immunoprecipitation datasets of different densities and sizes.Findings4N was applied on the immunoprecipitation dataset that was presented by the authors of the original 3N in Cell 145:787-799, 2011. The test with our method shows that it can reproduce the original clustering results with fewer manually adapted parameters and, in addition, gives direct insight into the complex-complex interactions. We also tested 4N on the human "Tip49a/b" dataset. We conclude that 4N can handle the contaminants and can correctly infer complexes from this very dense dataset. Further tests were performed on two artificial datasets of different sizes. We proved that the method predicts the reference complexes in the two artificial datasets with high accuracy, even when the number of samples is reduced.Conclusions4N has been implemented in R. We provide the sourcecode of 4N and a user-friendly toolbox including two example calculations. Biologists can use this 4N-toolbox even if they have a limited knowledge of R. There are only a few tuning parameters to set, and each of these parameters has a biological interpretation. The run times for medium scale datasets are in the order of minutes on a standard desktop PC. Large datasets can typically be analyzed within a few hours.
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Project description:The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.
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Project description:Human papilloma virus (HPV) is a serious threat to human life globally with over 100 genotypes including cancer causing high risk HPVs. Study on protein interaction maps of pathogens with their host is a recent trend in 'omics' era and has been practiced by researchers to find novel drug targets. In current study, we construct an integrated protein interaction map of HPV with its host human in Cytoscape and analyze it further by using various bioinformatics tools. We found out 2988 interactions between 12 HPV and 2061 human proteins among which we identified MYLK, CDK7, CDK1, CDK2, JAK1 and 6 other human proteins associated with multiple viral oncoproteins. The functional enrichment analysis of these top-notch key genes is performed using KEGG pathway and Gene Ontology analysis, which reveals that the gene set is enriched in cell cycle a crucial cellular process, and the second most important pathway in which the gene set is involved is viral carcinogenesis. Among the viral proteins, E7 has the highest number of associations in the network followed by E6, E2 and E5. We found out a group of genes which is not targeted by the existing drugs available for HPV infections. It can be concluded that the molecules found in this study could be potential targets and could be used by scientists in their drug design studies.
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