Multivariate Lévy Models by Linear Combination: Estimation

In this paper we propose a simple and effective two-step procedure to estimate the multivariate Lévy model introduced by Ballotta and Bonfiglioli (2012). We assess our estimation approach via simulations, comparing the results with those obtained through a standard but more computationally intensive one-step maximum likelihood estimation. The proposed method is then applied to the computation of the intra-horizon Value at Risk for a portfolio of assets following the model under consideration

Association of human cytomegalovirus proteins IRS1 and TRS1 with the viral DNA polymerase accessory subunit UL44

Multiple proteins interacting with DNA polymerases orchestrate DNA replication. Human cytomegalovirus (HCMV) encodes a DNA polymerase that includes the presumptive processivity factor UL44. UL44 is structurally homologous to the eukaryotic DNA polymerase processivity factor proliferating cell nuclear antigen (PCNA), which interacts with numerous proteins. Previous proteomic analysis has identified the HCMV protein IRS1 as a candidate protein interacting with UL44. Nuclease-resistant reciprocal co-immunoprecipitation of UL44 with IRS1 and with TRS1, which has an amino terminus identical to that of IRS1, was observed from lysate of cells infected with viruses expressing epitope-tagged UL44, epitope-tagged IRS1 or epitope-tagged TRS1. Western blotting of protein immunoprecipitated from infected cell lysate indicated that epitope-tagged IRS1 and TRS1 do not associate simultaneously with UL44. Glutathione S-transferase pull-down experiments indicated that IRS1 and TRS1 interact with UL44 via a region that is identical in both proteins. Taken together, these data suggest that IRS1 and TRS1 may compete for association with UL44 and may affect UL44 function differentially

A quantitative LumiFluo assay to test inhibitory compounds blocking p53 degradation induced by human papillomavirus oncoprotein E6 in living cells

High-risk human papillomaviruses (HR-HPVs) are the causative agents for the onset of several epithelial cancers in humans. The deregulated expression of the viral oncoproteins E6 and E7 is the driving force sustaining the progression of malignant transformation in pre-neoplastic lesions. Targeting the viral E6 oncoprotein through inhibitory compounds can counteract the survival of cancer cells due to the reactivation of p53-mediated pathways and represents an intriguing strategy to treat HPV-associated neoplasias. Here, we describe the development of a quantitative and easy-to-perform assay to monitor the E6-mediated degradation of p53 in living cells to be used for small-molecule testing. This assay allows to unbiasedly determine whether a compound can protect p53 from the E6-mediated degradation in cells, through a simple 3-step protocol. We validated the assay by testing two small molecules, SAHA and RITA, reported to impair the E6-mediated p53 degradation. Interestingly, we observed that only SAHA efficiently rescued p53, while RITA could not provide the same degree of protection. The possibility to specifically and quantitatively monitor the ability of a selected compound to rescue p53 in a cellular context through our LumiFluo assay could represent an important step towards the successful development of anti-HPV drugs

A Point Mutation in a Herpesvirus Polymerase Determines Neuropathogenicity

Infection with equid herpesvirus type 1 (EHV-1) leads to respiratory disease, abortion, and neurologic disorders in horses. Molecular epidemiology studies have demonstrated that a single nucleotide polymorphism resulting in an amino acid variation of the EHV-1 DNA polymerase (N752/D752) is significantly associated with the neuropathogenic potential of naturally occurring strains. To test the hypothesis that this single amino acid exchange by itself influences neuropathogenicity, we generated recombinant viruses with differing polymerase sequences. Here we show that the N752 mutant virus caused no neurologic signs in the natural host, while the D752 virus was able to cause inflammation of the central nervous system and ataxia. Neurologic disease induced by the D752 virus was concomitant with significantly increased levels of viremia (p = 0.01), but the magnitude of virus shedding from the nasal mucosa was similar between the N752 and D752 viruses. Both viruses replicated with similar kinetics in fibroblasts and epithelial cells, but exhibited differences in leukocyte tropism. Last, we observed a significant increase (p < 0.001) in sensitivity of the N752 mutant to aphidicolin, a drug targeting the viral polymerase. Our results demonstrate that a single amino acid variation in a herpesvirus enzyme can influence neuropathogenic potential without having a major effect on virus shedding from infected animals, which is important for horizontal spread in a population. This observation is very interesting from an evolutionary standpoint and is consistent with data indicating that the N752 DNA pol genotype is predominant in the EHV-1 population, suggesting that decreased viral pathogenicity in the natural host might not be at the expense of less efficient inter-individual transmission

Assessing the druggability of protein-protein interactions by a supervised machine-learning method

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM).</p> <p>Results</p> <p>To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies; these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%; specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways.</p> <p>Conclusion</p> <p>Using the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.</p

A domain-based approach to predict protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.</p> <p>Results</p> <p>DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.</p> <p>Conclusion</p> <p>We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.</p

Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine.</p> <p>Results</p> <p>To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS). Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs) retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM). Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV) proteins identified to date.</p> <p>Conclusions</p> <p>The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data.</p

Discovery of novel targets for multi-epitope vaccines: Screening of HIV-1 genomes using association rule mining

© 2009 Paul and Piontkivska; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens