Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules.
Ontology highlight
ABSTRACT:
SUBMITTER: Rimbault C
PROVIDER: S-EPMC6778148 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
Publications
Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules.
Rimbault Charlotte C Maruthi Kashyap K Breillat Christelle C Genuer Camille C Crespillo Sara S Puente-Muñoz Virginia V Chamma Ingrid I Gauthereau Isabel I Antoine Ségolène S Thibaut Coraline C Tai Fabienne Wong Jun FWJ Dartigues Benjamin B Grillo-Bosch Dolors D Claverol Stéphane S Poujol Christel C Choquet Daniel D Mackereth Cameron D CD Sainlos Matthieu M
Nature communications 20191004 1
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 outsi ...[more]
Similar Datasets
Project description:Bromo and extra terminal (BET) proteins (BRD2, BRD3, BRD4, and BRDT) are transcriptional regulators required for efficient expression of several growth promoting and antiapoptotic genes as well as for cell-cycle progression. BET proteins are recruited on transcriptionally active chromatin via their two N-terminal bromodomains (BRD), a protein interaction module that specifically recognizes acetylated lysine residues in histones H3 and H4. Inhibition of the BET-histone interaction results in transcriptional downregulation of a number of oncogenes, providing a novel pharmacologic strategy for the treatment of cancer. Here, we present a potent and highly selective dihydroquinazoline-2-one inhibitor, PFI-1, which efficiently blocks the interaction of BET BRDs with acetylated histone tails. Cocrystal structures showed that PFI-1 acts as an acetyl-lysine (Kac) mimetic inhibitor efficiently occupying the Kac binding site in BRD4 and BRD2. PFI-1 has antiproliferative effects on leukemic cell lines and efficiently abrogates their clonogenic growth. Exposure of sensitive cell lines with PFI-1 results in G1 cell-cycle arrest, downregulation of MYC expression, as well as induction of apoptosis and induces differentiation of primary leukemic blasts. Intriguingly, cells exposed to PFI-1 showed significant downregulation of Aurora B kinase, thus attenuating phosphorylation of the Aurora substrate H3S10, providing an alternative strategy for the specific inhibition of this well-established oncology target.
| S-EPMC3673830 | biostudies-literature
Project description:BACKGROUND: The timing of the origin of introns is of crucial importance for an understanding of early genome architecture. The Exon theory of genes proposed a role for introns in the formation of multi-exon proteins by exon shuffling and predicts the presence of conserved splice sites in ancient genes. In this study, large-scale analysis of potential conserved splice sites was performed using an intron-exon database (ExInt) derived from GenBank. RESULTS: A set of conserved intron positions was found by matching identical splice sites sequences from distantly-related eukaryotic kingdoms. Most amino acid sequences with conserved introns were homologous to consensus sequences of functional domains from conserved proteins including kinases, phosphatases, small GTPases, transporters and matrix proteins. These included ancient proteins that originated before the eukaryote-prokaryote split, for instance the catalytic domain of protein phosphatase 2A where a total of eleven conserved introns were found. Using an experimental setup in which the relation between a splice site and the ancientness of its surrounding sequence could be studied, it was found that the presence of an intron was positively correlated to the ancientness of its surrounding sequence. Intron phase conservation was linked to the conservation of the gene sequence and not to the splice site sequence itself. However, no apparent differences in phase distribution were found between introns in conserved versus non-conserved sequences. CONCLUSION: The data confirm an origin of introns deep in the eukaryotic branch and is in concordance with the presence of introns in the first functional protein modules in an 'Exon theory of genes' scenario. A model is proposed in which shuffling of primordial short exonic sequences led to the formation of the first functional protein modules, in line with hypotheses that see the formation of introns integral to the origins of genome evolution.
| S-EPMC1800838 | biostudies-other
Project description:It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a "module" in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN) by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a "module". In a self-consistent manner, proteins are grouped into "functional roles" if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD) and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network's structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function prediction algorithms.
| S-EPMC2813263 | biostudies-literature
Project description:The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN's community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).
| S-EPMC5764996 | biostudies-literature
Project description:Cancer is a disease associated with the deregulation of multiple gene networks. Microarray data has permitted researchers to identify gene panel markers for diagnosis or prognosis of cancer but these are not sufficient to make specific mechanistic assertions about phenotype switches. We propose a strategy to identify putative mechanisms of cancer phenotypes by protein-protein interactions (PPI). We first extracted the logic status of a PPI via the relative expression of the corresponding gene pair. The joint association of a gene pair on a cancer phenotype was calculated by entropy minimization and assessed using a support vector machine. A typical predictor is "If Src high-expression, and Cav-1 low-expression, then cancer." We achieved 90% accuracy on test data with a majority of predictions associated with the MAPK pathway, focal adhesion, apoptosis and cell cycle. Our results can aid in the development of phenotype discrimination biomarkers and identification of putative therapeutic interference targets for drug development.
| S-EPMC2865773 | biostudies-literature
Project description:Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein-protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4(+) T cells. The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ?75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease.
| S-EPMC3874207 | biostudies-literature
Project description:A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity.
| S-EPMC5701033 | biostudies-literature
Project description:Gene expression profiles have been frequently integrated with the human protein interactome to uncover functional modules under specific conditions like disease state. Beyond traditional differential expression analysis, differential co-expression analysis has emerged as a robust approach to reveal condition-specific network modules, with successful applications in a few human disease studies. Hepatocellular carcinoma (HCC), which is often interrelated with the Hepatitis C virus, typically develops through multiple stages. A comprehensive investigation of HCC progression-specific differential co-expression modules may advance our understanding of HCC's pathophysiological mechanisms.Compared with differentially expressed genes, differentially co-expressed genes were found more likely enriched with Hepatitis C virus binding proteins and cancer-mutated genes, and they were clustered more densely in the human reference protein interaction network. These observations indicated that a differential co-expression approach could outperform the standard differential expression network analysis in searching for disease-related modules. We then proposed a differential co-expression network approach to uncover network modules involved in HCC development. Specifically, we discovered subnetworks that enriched differentially co-expressed gene pairs in each HCC transition stage, and further resolved modules with coherent co-expression change patterns over all HCC developmental stages. Our identified network modules were enriched with HCC-related genes and implicated in cancer-related biological functions. In particular, APC and YWHAZ were highlighted as two most remarkable genes in the network modules, and their dynamic interaction partnership was resolved in HCC development.We demonstrated that integration of differential co-expression with the protein interactome could outperform the traditional differential expression approach in discovering network modules of human diseases. In our application of this approach to HCC's gene expression data, we successfully identified subnetworks with marked differential co-expression in individual HCC stage transitions and network modules with coherent co-expression change patterns over all HCC developmental stages. Our results shed light on subtle HCC mechanisms, including temporal activation and dismissal of pivotal functions and dynamic interaction partnerships of key genes.
| S-EPMC4029569 | biostudies-literature
Project description:Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi's sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species.
| S-EPMC2731838 | biostudies-literature
Project description:Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.
| S-EPMC3539715 | biostudies-literature