Abstract

A quantum information theory is derived for multidimensional signals scaling. Dynamical data modeling methodology is described for decomposing a signal in a coupled structure of binding synergies, in scale-space. Mass conservation principle, along with a generalized uncertainty relation, and the scale-space wave propagation lead to a polynomial decomposition of information. Statistical map of data, through dynamical cascades, gives an effective way of coding and assessing its control structure. Using a multi-scale approach, the scale-space wave information propagation is utilized in computing stochastic resonance synergies (SRS), and a data ensemble is conceptualized within an atomic structure. In this paper, we show the analysis of multidimensional data scatter, exhibiting a point scaling property. We discuss applications in image processing, as well as, in neuroimaging. Functional neuro-cortical mapping by multidimensional scaling is explained for two behaviorally correlated auditory experiments, whose BOLD signals are recorded by fMRI. The point scaling property of the information flow between the signals recorded in those two experiments is analyzed in conjunction with the cortical feature detector findings and the auditory tonotopic map. The brain wave nucleons from an EEG scan, along with a distance measure of synchronicity of the brain wave patterns, are also explained.

Keywords

Evaluation, Inverse Molecular Design Algorithm, Model Binding Site, In silico predicted, computer-aided molecular designed CTLA-4 blockador, increasement, antigen-specific CD8+ T-cells, inprevaccinated patients, melanoma, new cluster, algorithms, Large-Scale Protein-Ligand Docking experiment, inverse design, scoring function, protein-ligand interaction, cytochrome c peroxidase, dead-end elimination, drug design

Abstract

An increasing number of studies are aimed at modeling cellular environments in a comprehensive and realistic fashion. A major challenge in these efforts is how to bridge spatial and temporal scales over many orders of magnitude. Furthermore, there are additional challenges in integrating different aspects ranging from questions about biomolecular stability in crowded environments to the description of reactive processes on cellular scales. In this review, recent studies with models of biomolecules in cellular environments at different levels of detail are discussed in terms of their strengths and weaknesses. In particular, atomistic models, implicit representations of cellular environments, coarse-grained and spheroidal models of biomolecules, as well as the inclusion of reactive processes via reaction-diffusion models are described. Furthermore, strategies for integrating the different models into a comprehensive description of reaching new levels of realism in Modeling Biological hypermolecules on Glioma Growth Morphology generation of a MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma are discussed.

Keywords

Reaching New Levels; Realism in Modeling; Biological hypermolecules; Glioma Growth; Morphology generation; MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D); mimicking activator; PF-3512676; GM-CSF; clinical outcome; metastatic melanoma.

Abstract

The use of pharmacologically active short peptide sequences is a better option in cancer therapeutics than the full-length protein. Here we report one such 44-mer peptide sequence of SMAR1 (TAT-SMAR1 wild type, P44) that retains the tumor suppressor activity of the full-length protein. The protein transduction domain of human immunodeficiency virus, type 1, Tat protein was used here to deliver the 33-mer peptide of SMAR1into the cells. P44 peptide could efficiently activate p53 by mediating its phosphorylation at serine 15, resulting in the activation of p21 and in effect regulating cell cycle checkpoint. In vitro phosphorylation assays with point-mutated P44-derived peptides suggested that serine 347 of SMAR1 was indispensable for its activity and represented the substrate motif for the protein kinase C family of proteins. Using xenograft nude mice models, we further demonstrate that P44 was capable of inhibiting tumor growth by preventing cellular proliferation. P44 treatment to tumor-bearing mice prevented the formation of poorly organized tumor vasculature and an increase in hypoxia-inducible factor-1alpha expression, both being signatures of tumor progression. The chimeric TAT-SMAR1-derived peptide, P44, thus has a strong therapeutic potential as an anticancer drug.Abstract: The p53 and nuclear factor κB (NF-κB) pathways play crucial roles in human cancer development. Simultaneous targeting of both pathways is an attractive therapeutic strategy against cancer. The use of pharmacologically active short peptide sequences has prooven to be a better option in cancer therapeutics than the full-lengthprotein. It has been previously report ed one such 44-mer peptide sequence of SMAR1 (TAT-SMAR1 wild type, P44) that retains the tumor suppressor activity of the full-length protein.P44 peptide could efficiently activate p53 by mediating its phosphorylation at serine15, resulting in the activation of p21 and in effect regulating cell cycle checkpoint. In vitrophosphorylation assays with point-mutated P44-derived pep-tides suggested that serine 347 of SMAR1 was indispensable forits activity and represented the substrate motif for the proteinkinase C family of proteins. In this Research Scientific Project we generated Molecular dynamics and mechanistic in silico discovery simulations of a novel chemo-SMAR1-engineered p53 and NF-κB derived P44 cyclotidomimic agonisitic pharmacoligand P44 dual targeting hyperstructure for the activation of the p53 tumor suppressor pathway.

Keywords

Molecular dynamics simulations, in silico discovery, novel chemo-hyperstructure, drug discovery, dual targeting, NF-κB, pathways, activation, p53 tumor suppressor pathway, engineered, P44 cyclotidomimic, agonisitic, mechanistic, pharmacoligand, Molecular dynamics simulations and drug discovery, Keywords: molecular dynamics simulations, computer-aided drug discovery, cryptic binding sites, allosteric binding sites, virtual screening, free-energy prediction

Abstract

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.Computer designed of a Safe and immunogenic pharmacophoric activator mimicking physicochemical properties of the MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) inadjuvantwith PF-3512676 and GM-CSF with promising clinical outcome in metastatic melanoma using a new cluster of algorithms and a Ligand-Based Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine.

Keywords

Towards Structural Systems, Pharmacology Study, Complex Diseases, Personalized Medicine, Computer designed, immunogenic, pharmacophoric, activator, mimicking, physicochemical, properties, MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D), adjuvant, PF-3512676, GM-CSF, promising, clinical outcome, metastatic melanoma, new cluster of algorithms. Ligand-Based, Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine.

Abstract

The extraction of information from a quantum system unavoidably implies a modification of the measured system itself. In this framework partial measurements can be carried out in order to extract only a portion of the information encoded in a quantum system, at the cost of inducing a limited amount of disturbance. Here we analyze experimentally the dynamics of sequential partial measurements carried out on a quantum system, focusing on the trade-off between the maximal information extractable and the disturbance. In particular we implement two sequential measurements observing that, by exploiting an adaptive strategy, is possible to find an optimal trade-off between the two quantities for the testing of sequential quantum measurements on Glioma Growth Morphology for the generation of a MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma.

Keywords

Testing sequential; quantum measurements; maximal knowledge; Glioma Growth; Morphology; MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase; (368-376, 370D); mimicking activator; PF-3512676; GM-CSF; clinical outcome; metastatic melanoma;

Abstract

The logistic regression model has been become commonly used to study the association between a binary response variable; it is widespread application rests on its easy application and interpretation. The subject of assessment of goodness-of-fit in logistic regression model has attracted the attention of many scientists and researchers. Goodness-of-fit tests are methods to determine the suitability of the fitted model. Many of methods proposed and discussed for assessing goodness-of fit in logistic regression model, however, the asymptotic distribution of goodness-of-fit statistics are less examine, it is need more investigated. This work, will focus on assessing the behavior of asymptotic distribution of goodness-of-fit tests, also make comparison between global goodness-of-fit tests. This study, will also focus on evaluating it by simulation of asymptomatic distribution of on Glioma Growth Morphology for the generation of a Goodness-of-MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking Fit Tests in Logistic Regression Model activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma.

Keywords

Asymptomatic Distribution; Glioma Growth Morphology; Goodness-of-MART-1; (26-35,27L), gp100 (209-217, 210M), tyrosinase; (368-376, 370D); mimicking Fit Tests; Logistic Regression; Model activator; PF-3512676; GM-CSF; clinical outcome; metastatic melanoma; Logistic Regression Model, Goodness-of-Fit Tests.

Abstract

These small cysteine-rich peptides are active against bacteria, fungi and viruses. Plectasin—the first defensin has been isolated from a fungus, the saprophytic ascomycetePseudoplectania nigrella. Polypeptides having antimicrobial activity may be capable of reducing the number of living cells of Bacillus subtilis (ATCC 6633). Plectasin has primary, secondary and tertiary structures that closely resemble those of defensins found in spiders, scorpions, dragonflies and mussels. Recombinant plectasin was produced at a very high, and commercially viable, yield and purity. In vitro, the recombinant peptide was especially active againstStreptococcus pneumoniae, including strains resistant to conventional antibiotics. Plectasin showed extremely low toxicity in mice, and cured them of experimental peritonitis and pneumonia caused by S. pneumoniae as efficaciously as vancomycin and penicillin. These findings identify fungi as a novel source of antimicrobial defensins, and show the therapeutic potential of plectasin. They also suggest that the defensins of insects, molluscs and fungi arose from a common ancestral gene. The Poisson-Boltzmann equation models the electrostatic potential generated by fixed charges on a polarizable solute immersed in an ionic solution. This approach is often used in computational structural biology to estimate the electrostatic energetic component of the assembly of molecular biological systems. In the last decades, the amount of data concerning proteins and other biological macromolecules has remarkably increased. To fruitfully exploit these data, a huge computational power is needed as well as software tools capable of exploiting it. It is therefore necessary to move towards high performance computing and to develop parallel implementations of already existing and of novel algorithms. In this Research and Scientific Project, we propose the implementation of a full Poisson-Boltzmann solver based on a finite-difference scheme using different and combined parallel schemes and in particular a mixed MPI-CUDA implementation. Here, we have for the first time discovered a A Rational predicted pDRS-18-plectasin peptide-mimetic pharmacophore comprising antibiotic properties with therapeutic potential from a saprophytic fungus using BiogenetoligandorolTM drug discovery process combined MPI-CUDA parallel solution of linear and nonlinear Poisson-Boltzmann equation. In this article we are discussing the nature and mechanism of the huge amount of heat generation in Megawatts Energy Catalyzers (E-cat) of Andrea Rossi that are able to change the energetics of our civilization in general. These processes are new effects of Unitary Quantum Theory and do not relate to either chemical or nuclear reactions or phase transfer. The Poisson-Boltzmann equation models the electrostatic potential generated by fixed charges on a polarizable solute immersed in an ionic solution. This approach is often used in computational structural biology to estimate the electrostatic energetic component of the assembly of molecular biological systems. In the last decades, the amount of data concerning proteins and other biological macromolecules has remarkably increased. To fruitfully exploit these data, a huge computational power is needed as well as software tools capable of exploiting it. It is therefore necessary to move towards high performance computing and to develop proper parallel implementations of already existing and of novel algorithms. Nowadays, workstations can provide an amazing computational power: up to 10 TFLOPS on a single machine equipped with multiple CPUs and accelerators such as Intel Xeon Phi or GPU devices. The actual obstacle to the full exploitation of modern heterogeneous resources is efficient parallel coding and porting of software on such architectures. In this paper, we propose the implementation of a full Poisson-Boltzmann solver based on a finite-difference scheme using different and combined parallel schemes and in particular a mixed MPI-CUDAimplementation. Results show great speedups using Unitary Quantum Rational Enigmatic E-Cat interactive structure of Andrea Rossi Theory as a cross-docking based integrating approach for the computer assisted generation of pDRS-18-plectasin peptide-mimetic pharmacophore comprising antibiotic properties with therapeutic potential from a saprophytic fungus.

Keywords

Enigmatic, E-Cat of Andrea Rossi; Unitary Quantum; Rational cross-docking Theory; interactive structure; integrating approach; computer assisted generation; pDRS-18-plectasin; peptide-mimetic; pharmacophore; antibiotic properties; therapeutic potential; saprophytic fungus, E-Cat, Nickel Powder, Heterogeneous Catalysis, Quantum Harmonic Oscillator, Unitary Quantum Mechanics.

Abstract

Next-generation molecular force fields deliver accurate descriptions of non-covalent interactions by employing more elaborate functional forms than their predecessors. Much work has been dedicated to improving the description of the electrostatic potential (ESP) generated by these force fields. A common approach to improving the ESP is by augmenting the point charges on each center with higher-order multipole moments. The resulting anisotropy greatly improves the directionality of the non-covalent bonding, with a concomitant increase in computational cost. In this work, we develop an efficient strategy for enumerating multipole interactions, by casting an efficient spherical harmonic based approach within a particle mesh Ewald (PME) framework. Although the derivation involves lengthy algebra, the final expressions are relatively compact, yielding an approach that can efficiently handle both finite and periodic systems without imposing any approximations beyond PME. Forces and torques are readily obtained, making our method well suited to modern molecular dynamics simulations. Drug discovery programs launched by the Medicines for Malaria Venture and other product-development partnerships have culminated in the development of promising new antimalarial compounds such as the synthetic peroxide OZ439 (Charman et al., 2011) and the spiroindolone NITD 609 (Rottmann et al., 2010), which are currently undergoing clinical trials. In spite of these recent successes, it is pivotal to maintain early phase drug discovery to prevent the antimalarial drug development pipeline from draining. Due to the propensity of the parasite to become drug-resistant (Muller and Hyde, 2010; Sa et al., 2011), the need for new antimalarial chemotypes will persist until the human-pathogenic Plasmodium spp. are eventually eradicated. Rational post-genomic drug discovery is based on the screening of large chemical libraries – either virtually or in high-throughput format – against a given target enzyme of the parasite. Experimental tools to validate candidate drug targets are limited for the malaria parasites. Gene silencing by RNAi does not seem to be feasible (Baum et al., 2009). Gene replacement with selectable markers is (Triglia et al., 1998), but it is inherently problematic to call a gene essential from failing to knock it out. However, none of the reverse genetic methods is practicable at the genome-wide scale. On the other hand Mestres et al. (Cases et al., 2005; Mestres et al., 2006) have annotated a library of molecules targeting NHRs. Using a hierarchical classification for 200.000 ligands and 5 receptors, chemogenomic links bridging ligand to target space can be easily recovered to distinguish selective from promiscuous scaffolds. Using Shannon Entropy descriptors (SHED) based on the distribution of atom-centred feature pairs, any compound collection can be screened to identify hits presenting SHED distances to a reference NHR ligand beyond a defined threshold and therefore likely to share the same NHR profile. Here, we successfully applied a machine-learning algorithm using Bayesian statistics (Xia et al., 2004) to predict target profiles from extended connectivity conserved motif like binding site active pharmacophore fingerprints of selected compounds from the biologically annotated free and non commercial databases (Nidhi et al., 2006) in resulting finally to an efficient algorithm for multipole energies and derivatives based on spherical harmonics and extensions on a particle mesh Ewald shannon entropy descriptor (SHED) for the in silico prediction of an annotated suitable lead chemo-recored compound as a potent computer predicted inhibitor comprising potential hyper-mimicking activities to 5 conserved anti-plasmodium peptides.

Keywords

An efficient algorithm; multipole energies; derivatives; spherical harmonics; extensions to particle; mesh Ewald; shannon entropy descriptor (SHED); in silico prediction; lead chemo-recored; compound; potent; computer predicted; inhibitor;hyper-mimicking; conserved anti-plasmodium peptides.

Abstract

Background

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation.

Results

Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models.KIF20A (RAB6KIFL) belongs to the kinesin superfamilyof motor proteins, which play critical roles in the traffickingof molecules and organelles during the growth of pancreatic cancer.Immunotherapy using a previously identified epitope peptide forKIF20A is expected to improve clinical outcomes. A phase I clinicaltrial combining KIF20A-derived peptide with gemcitabine (GEM) was therefore conducted among patients with advancedpancreatic cancer who had received prior therapy such as chemotherapyand/or radiotherapy. Despite, huge importance of the field, no dedicated AVP resource is available. In the present Research Scientific Project , we have collected 1245 peptides with antiviral activity targeting important human viruses like influenza, HIV, HCV and SARS, etc. After removing redundant peptides, 1056 peptides were divided into 951 training and 105 validation data sets. We have exploited various peptides sequence features, i.e. motifs and alignment followed by amino acid composition and physicochemical properties during 5-fold cross validation using Support Vector Machine. Physiochemical properties-based model achieved maximum 85% accuracy and 0.70 Matthew’s Correlation Coefficient (MCC). Therefore, AVPpred—the first web server for predicting the highly effective AVPs would certainly be helpful to researchers working on peptide-based antiviral development. The web server is freely available at http://crdd.osdd.net/ servers/avpp. Here, in Biogenea we have discovered for the first time an in silico KIF20A-derived Peptide mimic designed poly-chemo-pharmacophoric macroscaffold as a future super-antagonist for the treatment of PatientsWith Advanced Pancreatic Cancer.An in silico KIF20A-derived Peptide agonistic mimicking sited and computer-aided designed poly-chemo-scaffold as an innovative drug-like molecule comprising potential clinical hyper-inhibitor properties in Patients With Advanced Pancreatic Cancer when combined with Gemcitabine.Asymmetric bagging and feature selection for activities prediction of drug molecules.

Keywords

Asymmetric, bagging, feature, selection, activities, prediction, in silico, KIF20A-derived, Peptide, agonistic, drug molecules, mimicking, computer-aided, designed, poly-chemo-scaffold, innovative, drug-like, molecule, comprising, potential, clinical, hyper-inhibitor, Advanced Pancreatic Cancer, Gemcitabine.

Abstract

Orthodox quantum mechanics is a highly successful theory despite its serious conceptual flaws. It renounces realism, implies a kind of action-at-a-distance and is incompatible with determinism. Orthodox quantum mechanics states that Schrödinger’s equation (a deterministic law) governs spontaneous processes while measurement processes are ruled by probability laws. It is well established that time dependent perturbation theory must be used for solving problems involving time. In order to account for spontaneous processes, this last theory makes use of laws valid only when measurements are performed. This incoherence seems absent from the literature and may introduce innovative Orthodox Quantum Mechanics for the in silico KIF20A-derived Peptide agonistic drug molecules with mimicking sited and computer-aided designed properties on a poly-chemo-scaffold as an innovative drug-like scaffolds comprising potential clinical hyper-inhibitor properties in Patients With Advanced Pancreatic Cancer when combined with Gemcitabine.

Keywords

Success and Incoherence; Orthodox Quantum Mechanics; in silico; KIF20A-derived Peptide agonistic; drug molecules; mimicking sited; computer-aided designed on a poly-chemo-scaffold as an innovative drug-like scaffolds; clinical hyper-inhibitor; Advanced Pancreatic Cancer; when combined with Gemcitabine. Quantum Measurements―Time Dependent Perturbation Theory, Success and Incoherence; Orthodox Quantum Mechanics; in silico; KIF20A-derived Peptide; agonistic drug molecules; mimicking sited; computer-aided; poly-chemo-scaffold; innovative drug-like molecule; clinical hyper-inhibitor;