REFINING LITERATURE CURATED PROTEIN INTERACTIONS USING EXPERT OPINIONS

FoxJ1 is a forkhead transcription factor expressed in multiple tissues during development and a major regulator of cilia development. FoxJ1(-/-) mice present with defects in odontogenesis, and we correlate these defects to hierarchical interactions between homeodomain factors Pitx2 and Dlx2 with FoxJ1 in regulating their expression through direct physical interactions. Chromatin immunoprecipitation assays reveal endogenous Pitx2 and Dlx2 binding to the Dlx2 promoter and Dlx2 binding to the FoxJ1 promoter as well as Dlx2 and FoxJ1 binding to the amelogenin promoter. PITX2 activation of the Dlx2 promoter is attenuated by a direct Dlx2 physical interaction with PITX2. Dlx2 autoregulates its promoter, and Dlx2 transcriptionally activates the downstream gene FoxJ1. Dlx2 and FoxJ1 physically interact and synergistically regulate both Dlx2 and FoxJ1 promoters. Dlx2 and FoxJ1 also activate the amelogenin promoter, and amelogenin is required for enamel formation and late stage tooth development. FoxJ1(-/-) mice maxillary and mandibular incisors are reduced in length and width and have reduced amelogenin expression. FoxJ1(-/-) mice show a reduced and defective ameloblast layer, revealing a biological effect of these transcription factor hierarchies during tooth morphogenesis. These transcriptional mechanisms may contribute to other developmental processes such as neuronal, pituitary, and heart development.

BackgroundGenes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour.ResultsC3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis.ConclusionsOur approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network.

Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

The Impact of Gene Expression Regulation on Evolution of Extracellular Signaling Pathways

BackgroundPredicting novel interactions between HIV-1 and human proteins contributes most promising area in HIV research. Prediction is generally guided by some classification and inference based methods using single biological source of information.ResultsIn this article we have proposed a novel framework to predict protein-protein interactions (PPIs) between HIV-1 and human proteins by integrating multiple biological sources of information through non negative matrix factorization (NMF). For this purpose, the multiple data sets are converted to biological networks, which are then utilized to predict modules. These modules are subsequently combined into meta-modules by using NMF based clustering method. The integrated meta-modules are used to predict novel interactions between HIV-1 and human proteins. We have analyzed the significant GO terms and KEGG pathways in which the human proteins of the meta-modules participate. Moreover, the topological properties of human proteins involved in the meta modules are investigated. We have also performed statistical significance test to evaluate the predictions.ConclusionsHere, we propose a novel approach based on integration of different biological data sources, for predicting PPIs between HIV-1 and human proteins. Here, the integration is achieved through non negative matrix factorization (NMF) technique. Most of the predicted interactions are found to be well supported by the existing literature in PUBMED. Moreover, human proteins in the predicted set emerge as ‘hubs’ and ‘bottlenecks’ in the analysis. Low p-value in the significance test also suggests that the predictions are statistically significant.

Pathogenic microorganisms exploit host cellular mechanisms and evade host defense mechanisms through molecular pathogen-host interactions (PHIs). Therefore, comprehensive analysis of these PHI networks should be an initial step for developing effective therapeutics against infectious diseases. Computational prediction of PHI data is gaining increasing demand because of scarcity of experimental data. Prediction of protein-protein interactions (PPIs) within PHI systems can be formulated as a classification problem, which requires the knowledge of non-interacting protein pairs. This is a restricting requirement since we lack datasets that report non-interacting protein pairs. In this study, we formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data. This eliminates the abovementioned requirement and enables us to predict new interactions without randomly labeling protein pairs as non-interacting. Domain-domain associations are used to filter the predicted results leading to 175 novel PHIs between 170 human proteins and 105 viral proteins. To compare our results with the state-of-the-art studies that use a binary classification formulation, we modified our settings to consider the same formulation. Detailed evaluations are conducted and our results provide more than 10 percent improvements for accuracy and AUC (area under the receiving operating curve) results in comparison with state-of-the-art methods.

Computational prediction of protein-protein interaction (PPI) from protein sequence is important as many cellular functions are made possible through PPI. The Protein Interaction Prediction Engine (PIPE) software suite was developed for such predictions. The specific location of interaction is predicted by the PIPE-Sites predictor, which depends on PIPE engine. This PIPE-Sites predictor is here updated through the use of a large high-quality dataset of known PPI sites. Additionally, a similarity-weighted score had been recently developed in PIPE4 and has been proven to be more accurate for the likelihood of PPI prediction. However, PIPE-Sites are shown to be ineffective when applied to similarity-weighted score data. Thus, we here propose and evaluate a new sequence-based PPI site prediction method, named Panorama. This new method leverages similarity-weighted score data to further increase performance over two different performance metrics when evaluated on both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{H}$</tex> . sapiens and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{S}$</tex> , cerevisiae PPI site data.

[2] has been proposed as a generalization of the idea of the phylogenetic prole in order to measure the evolutionary distance between proteins more precisely. Both methods basically stem from the assumption that functionally correlated proteins evolve in a correlated manner. In the mirror tree method, the intensity of the correlation between proteins is evaluated by Peason’s correlation coecient based on phylogenetic trees, but it has been pointed out that a number of false positives tend to be introduced in the prediction. This paper presents a new method to predict protein-protein interactions from the evolutionary information by using partial correlation coecien t in order to extract direct interactions rather than indirect interactions between proteins. Our method is successfully tested on its ability to predict physical protein interactions, from the comparison of phylogenetic trees of proteins. Using the dataset containing known interactions, we show that our approach removes false positives to a large extent and improves the specicit y for predicting physical protein-protein interactions. 2 Method

PRASA: An integrated web server that analyzes protein interaction types

Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks. However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%. As an application, we established an integrated human directional protein interaction network, including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. Directional protein interaction network was strongly supported by the known signaling pathways literature (with the 87.5% accuracy) and further analyses on the biological annotation, subcellular localization, and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs and a powerful tool to unravel the unknown signaling pathways.

Mitochondrial Ca2+ transport mediated by the uniporter complex (MCUC) plays a key role in the regulation of cell bioenergetics in both trypanosomes and mammals. Here we report that Trypanosoma brucei MCU (TbMCU) subunits interact with subunit c of the mitochondrial ATP synthase (ATPc), as determined by coimmunoprecipitation and split-ubiquitin membrane-based yeast two-hybrid (MYTH) assays. Mutagenesis analysis in combination with MYTH assays suggested that transmembrane helices (TMHs) are determinants of this specific interaction. In situ tagging, followed by immunoprecipitation and immunofluorescence microscopy, revealed that T. brucei ATPc (TbATPc) coimmunoprecipitates with TbMCUC subunits and colocalizes with them to the mitochondria. Blue native PAGE and immunodetection analyses indicated that the TbMCUC is present together with the ATP synthase in a large protein complex with a molecular weight of approximately 900 kDa. Ablation of the TbMCUC subunits by RNA interference (RNAi) significantly increased the AMP/ATP ratio, revealing the downregulation of ATP production in the cells. Interestingly, the direct physical MCU-ATPc interaction is conserved in Trypanosoma cruzi and human cells. Specific interaction between human MCU (HsMCU) and human ATPc (HsATPc) was confirmed in vitro by mutagenesis and MYTH assays and in vivo by coimmunoprecipitation. In summary, our study has identified that MCU complex physically interacts with mitochondrial ATP synthase, possibly forming an MCUC-ATP megacomplex that couples ADP and Pi transport with ATP synthesis, a process that is stimulated by Ca2+ in trypanosomes and human cells.IMPORTANCE The mitochondrial calcium uniporter (MCU) is essential for the regulation of oxidative phosphorylation in mammalian cells, and we have shown that in Trypanosoma brucei, the etiologic agent of sleeping sickness, this channel is essential for its survival and infectivity. Here we reveal that that Trypanosoma brucei MCU subunits interact with subunit c of the mitochondrial ATP synthase (ATPc). Interestingly, the direct physical MCU-ATPc interaction is conserved in T. cruzi and human cells.

The four C-terminal GLI-Krüppel type zinc fingers of YY1 have been identified as a transcriptional repression domain. Previous reports have proposed DNA-bending and activator-quenching mechanisms for this zinc finger-mediated repression. In addition, previous work indicated that p300 and CBP might be involved in YY1-mediated repression. We have analyzed these possible models for the zinc finger-mediated repression. The role of each zinc finger in the repression and DNA-binding functions was determined by using a structure-and-function approach. We show that zinc finger 2 of YY1 plays a central role in both DNA binding and transcriptional repression. However, a survey of a panel of YY1 mutants indicates that these two functions can be separated, which argues against the DNA-bending model for repression. We show that the physical interaction between YY1 and p300, a coactivator for CREB, is not sufficient for repression of CREB-mediated transcription. Our studies indicate that YY1 functions as an activator-specific repressor. Repression of CTF-1-directed transcription may be accomplished through direct physical interaction between YY1 and this activator. In contrast, physical interaction is not necessary for YY1 to repress Sp1- and CREB-mediated transcription. Rather, the repression likely reflects an ability of YY1 to interfere with communication between these activators and their targets within the general transcription machinery. Taken together, our results suggest that YY1 employs multiple mechanisms to achieve activator-specific repression.

Quaternion-based Unscented Kalman Filter for Robust Wrench Estimation of Human-UAV Physical Interaction

Determining which proteins interact together is crucial to a systems-level understanding of the cell. Recently, algorithms based on Direct Coupling Analysis (DCA) pairwise maximum-entropy models have allowed to identify interaction partners among paralogous proteins from sequence data. This success of DCA at predicting protein-protein interactions could be mainly based on its known ability to identify pairs of residues that are in contact in the three-dimensional structure of protein complexes and that coevolve to remain physicochemically complementary. However, interacting proteins possess similar evolutionary histories. What is the role of purely phylogenetic correlations in the performance of DCA-based methods to infer interaction partners? To address this question, we employ controlled synthetic data that only involve phylogeny and no interactions or contacts. We find that DCA accurately identifies the pairs of synthetic sequences that share evolutionary history. While phylogenetic correlations confound the identification of contacting residues by DCA, they are thus useful to predict interacting partners among paralogs. We find that DCA performs as well as phylogenetic methods to this end, and slightly better than them with large and accurate training sets. Employing DCA or phylogenetic methods within an Iterative Pairing Algorithm (IPA) allows to predict pairs of evolutionary partners without a training set. We further demonstrate the ability of these various methods to correctly predict pairings among real paralogous proteins with genome proximity but no known direct physical interaction, illustrating the importance of phylogenetic correlations in natural data. However, for physically interacting and strongly coevolving proteins, DCA and mutual information outperform phylogenetic methods. We finally discuss how to distinguish physically interacting proteins from proteins that only share a common evolutionary history.

The occurrence frequencies of bases A (adenine), C (cytosine, G (guanine), and T (thymine) occurring in the 1st, 2nd, and 3rd codon positions in the codon usage table of viral genes for the 339 human immunodeficiency virus (HIV) proteins compiled recently have been calculated and diagrammatized. For comparison, the corresponding diagrammatic representations for the 2681 human proteins from the codon usage table for primate genes are also presented. The analyzed results based on these characteristic diagrams indicate that considerably similar features have been found between HIV and human proteins for the 1st and 2nd codon positions; i.e., they are all occupied predominantly by purine, especially base A. However, a significant difference in the 3rd codon position between HIV and human proteins has been observed; i.e., human proteins are of high C + G content and low A + G content in the 3rd codon position, whereas the case is just the opposite for HIV proteins. The biological implication of such a duality on the codon bias of HIV against human proteins is discussed. It is suggested that the 1st and 2nd codon positions can be termed as the structure-determining position, and the 3rd codon position termed as the species-determining position. The diagrammatic representation and analysis method described here possess a great potential for the study of molecular evolution from the viewpoint of the genetic code for which data have been accumulated rapidly and will continue to grow at a much faster pace.