Influenza A virus is well known for its severe clinical consequences. Structurally, this virus is made up of a lipid bilayer embedded with HA, NA and M2 proteins and a core containing eight viral ribonucleoprotein (RNP) complexes. In a typical RNP complex, the nucleoprotein binds with RNA in a non specific manner. The nucleoprotein plays a vital role in transcription, replication, and packaging of RNA during infection. This study aims that NP of A/PR/8/34(H1N1) virus and A/NY/55/2004(H3N2) virus interact with different host proteins depending on cell lines and virus strains. Monoclonal antibodies targeting the nucleoprotein of these viruses have been used for immunoprecipitation and the interacting proteins were identified by mass spectrometry. Tow proteins from the cytoplasm (elongation factor 1 sigma, and Mov10 protein) and 3 proteins from the nucleus (heat shock protein70, hnRNP K protein, and anti alpha actinin 4) were found in all the viral infected cells, and were chosen for validation study. This study will help to understand the virus-host interactions in a better way and may open the gateway for the synthesis of new antiviral drugs which can block these interactions, hence controlling the infection.

Influenza A virus is well known for its severe clinical consequences. Structurally, this virus is made up of a lipid bilayer embedded with HA, NA and M2 proteins and a core containing eight viral ribonucleoprotein (RNP) complexes. In a typical RNP complex, the nucleoprotein binds with RNA in a non specific manner. The nucleoprotein plays a vital role in transcription, replication, and packaging of RNA during infection. This study aims that NP of A/PR/8/34(H1N1) virus and A/NY/55/2004(H3N2) virus interact with different host proteins depending on cell lines and virus strains. Monoclonal antibodies targeting the nucleoprotein of these viruses have been used for immunoprecipitation and the interacting proteins were identified by mass spectrometry. Tow proteins from the cytoplasm (elongation factor 1 sigma, and Mov10 protein) and 3 proteins from the nucleus (heat shock protein70, hnRNP K protein, and anti alpha actinin 4) were found in all the viral infected cells, and were chosen for validation study. This study will help to understand the virus-host interactions in a better way and may open the gateway for the synthesis of new antiviral drugs which can block these interactions, hence controlling the infection.

Understanding protein-protein interaction (PPI) is important in order to understand cellular processes. X-ray crystallography and mutagenesis, expensive methods both in time and resources, are the most reliable methods for detecting PPI. Computational approaches could, therefore, reduce resources and time spent on detecting PPIs. During this master thesis a method, cProQPred, was created for scoring how realistic coarse PPI models are. cProQPred use the machine learning method Random Forest trained on previously calculated features from the programs ProQDock and InterPred. By combining some of ProQDock’s features and the InterPred score from InterPred the cProQPred method generated a higher performance than both ProQDock and InterPred. This work also tried to predict the quality of the PPI model after refinement and the chance for a coarse PPI model to succeed at refinement. The result illustrated that the predicted quality of a coarse PPI model also was a relatively good prediction of the quality the coarse PPI model would get after refinement. Prediction of the chance for a coarse PPI model to succeed at refinement was, however, without success.

There are now numerous examples of post-translational modification with geranylgeranyl or farnesyl substituents. Once thought of as solely a mechanism for association of proteins with membranes, other functional aspects of protein prenylation have come to be appreciated. Although, in almost all instances, such proteins are membrane associated, they are often found to also engage in protein-protein interactions. In some instances, such interactions are critical aspects of prenylated protein trafficking. In this review, the role of prenylation in mediating protein-protein interactions will be considered. The hypothesis will be developed that such interactions occur through recognition of the prenyl group and a second domain, on the prenylated protein, by a heterodimeric protein partner.

No description available.

Human serum transferrin (hTF) is a bilobal glycoprotein that plays a central role in iron metabolism. Each lobe of hTF (N- and C-lobe) can reversibly bind a single ferric iron. Iron binds to hTF at neutral pH in the plasma; diferric hTF binds to specific hTF receptors (TFR) on the cell surface and the complex undergoes receptor mediated endocytosis. The pH within the endosome is lowered to ~5.6 and iron is released from hTF. Apo hTF remains bound to the TFR and recycles back to the cell surface. Upon fusion with the plasma membrane, apo hTF dissociates from the TFR and is free to bind more iron and continue the cycle. The iron release process is complicated by various factors which include pH, anions, a chelator, lobe-lobe cooperativity and interaction with the TFR. All of these influence iron release in a complex manner. Because they are intricately linked, it is difficult to determine the effect of any single parameter. We have utilized stopped-flow and steady-state fluorescence and urea gel electrophoresis to dissect the iron release process as a function of lobe-lobe interactions, the presence of the TFR, and changes in pH and salt concentration. Application of recombinant protein production and site-directed mutagenesis has allowed us to generate a variety of hTF constructs in which the iron status of each lobe is completely controlled. Thus, we have created authentic monoferric hTFs unable to bind iron in one lobe, diferric hTFs with iron locked in one lobe and diferric hTF in which iron can be removed from both lobes. Importantly, we have produced the soluble portion of the TFR (sTFR) to analyze interactions between hTF and the sTFR and to monitor iron release from hTF/sTFR complexes. Together, we are able to provide a more precise picture of iron release from the two lobes of hTF in the presence and absence of the TFR. Steady-state fluorescence emission scans and urea gel electrophoresis provide a qualitative evaluation of the iron status of each construct after a predetermined incubation in iron removal buffer (i.e. an endpoint). However, these techniques do not provide information regarding the kinetic pathway to reach that endpoint. Combined with stopped-flow fluorescence time-based kinetics, a more precise assessment of the iron release process has been obtained. We have determined that changes in pH and salt affect endpoint iron release from the C-lobe, but not the N-lobe, however, the kinetics of iron release from both lobes are highly sensitive to pH and salt. Kinetic analysis in the absence and presence of the sTFR reveals the complexity of the iron release process. In the absence of the sTFR, the kinetics of iron release are insensitive to the iron status of the opposite lobe. However, in the presence of the sTFR, the kinetics of iron release from both lobes are affected by the iron status of the opposite lobe. Determination of conformational changes induced by anion binding, lobe-lobe communication and sTFR interactions have now been confidently assigned. We have created kinetic models of iron release from diferric hTF ± the sTFR and incorporated specific events pertaining to anion binding, lobe-lobe communication and conformational changes associated with sTFR interactions. We provide irrefutable evidence that a critical role of the sTFR is to accelerate the rate of iron release from the C-lobe, while decreasing the rate of iron release from the N-lobe such that the two lobes effectively release iron on a time scale relevant to one cycle of endocytosis.

Protein-protein interactions modulate cellular processes in health and disease. However, tracing weak or rare associations or dissociations of proteins is not a trivial task. Kinases are often regulated through interaction partners and, at the same time, themselves regulate cellular interaction networks. The use of kinase domains for creating a synthetic sensor device that reads low concentration protein-protein interactions and amplifies them to a higher concentration interaction which is then translated into a FRET (Fluorescence Resonance Energy Transfer) signal is here proposed. To this end, DNA constructs for interaction amplification (split kinases), positive controls (intact kinase domains), scaffolding proteins and phosphopeptide - SH2-domain modules for the reading of kinase activity were assembled and expression protocols for fusion proteins containing Lyn, Src, and Fak kinase domains in bacterial and in cell-free systems were optimized. Also, two non-overlapping methods for measuring the kinase activity of these proteins were stablished and, finally, a protein-fragment complementation assay with the split-kinase constructs was tested. In conclusion, it has been demonstrated that features such as codon optimization, vector design and expression conditions have an impact on the expression yield and activity of kinase-based proteins. Furthermore, it has been found that the defined PURE cell-free system is insufficient for the active expression of catalytic kinase domains. In contrast, the bacterial co-expression with phosphatases produced active kinase fusion proteins for two out of the three tested Tyrosine kinase domains.

The mamallian target of rapamycin (mTOR) is a protein implicated in a variety of cellular processes involving growth and division. In the context of the brain, it regulates synaptic plasticity and axon elongation; its dysfunction is implicated in the pathogenesis of multiple complex, heterogeneous neurodegenerative diseases. These include, but are not limited to Alzheimer’s Disease (AD), autism spectrum disorder (ASD), and epilepsy. mTOR boasts a deeply complex and far-reaching signalling cascade, and its activity affects the expression levels of a large number of proteins. As such, investigation of the proteins with whom mTOR interacts is a pertinent endeavor to the advancement of understanding the complex pathogenesis of neurodegenerative disease. The complexity of this endeavor makes it a target well-poised for protein-protein interaction network (PPIN) analysis. Thus, using a previously recorded MS/MS dataset listing proteins whose expression levels change upon rapamycin administration, we set out to identify key proteins and characterize the properties of the mTOR interactome overall using a variety of toplogical measures and analytical techniques. Using such techniques, we found that the in the PPIN created from our data, a certain subset of proteins subjected the network to particular fragility. Namely, the kinless hubs, which have high within-module degree as well as a large participation coefficient, show vulnerability exceeding that of even conventionally defined hub. Some of these kinless hubs exhibit critical structural roles in the PPIN such that their removal damages the overall efficiency of communication within the network at an individually observable level. Work is ongoing to further investigate these proteins and the potential biological implications of their importance in the network described in the present study.

No description available.

Background: Genome-wide association studies (GWAS) prove to be a powerful approach to identify the genetic basis of various human[1] diseases. Here we take advantage of existing GWAS data and attempt to build a framework to understand the complex relationships among diseases. Specifically, we examined 49 diseases from all available GWAS with a cascade approach by exploiting network analysis to study the single nucleotide polymorphisms (SNP) effect on the similarity between different diseases. Proteins within perturbation subnetwork are considered to be connection points between the disease similarity networks. Results: shared disease subnetwork proteins are consistent, accurate and sensitive to measure genetic similarity between diseases. Clustering result shows the evidence of phenome similarity. Conclusion: our results prove the usefulness of genetic profiles for evaluating disease similarity and constructing disease relationship networks. / Master of Science