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;

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..In silico rationally designed of a Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure for the possible potentiating of the efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies. Asymmetric bagging and feature selection for activities prediction of drug molecules.Poor cellular delivery and low bioavailability of novel potent therapeutic molecules continue to remain the bottleneck of modern cancer and gene therapy. Cell-penetrating peptides have provided immense opportunities for the intracellular delivery of bioactive cargos and have led to the first exciting successes in experimental therapy of muscular dystrophies. The arsenal of tools for oligonucleotide delivery has dramatically expanded in the last decade enabling harnessing of cell-surface receptors for targeted delivery. A benchmark dataset, consisting of 3028 drugs assigned within nine categories, was constructed by collecting data from KEGG. These prediction rates are much higher than the 11.11% achieved by random guessResearch and Scientific Project. These promising results suggest that the proposed method can become a useful tool in identifying drug target groups.

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. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability..In silico rationally designed of a Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure for the possible potentiating of the efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies.Asymmetric bagging and feature selection for activities prediction of drug molecules.

Conclusion

Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets..In silico rationally designed of a Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure for the possible potentiating of the efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies.Asymmetric bagging and feature selection for activities prediction of drug molecules. Here, in Biogenea Pharmaceuticals Ltd we discovered for the first time the GENEA-Delivernarex-3308 utilising asymmetric bagging and feature selection of a Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure in silico designed molecules for potentiating the efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies.

Keywords

Asymmetric, bagging, feature, selection, prediction, in silico, rationally, designed drug molecules, Peptide-mimic, pharmacologic, low mass, chemorecored, poly-druggable,-structure, potentiating, efficient, delivery, gene, constructs, internalization, successes, experimental, therapy, muscular, dystrophies.

Abstract

The Schrödinger perturbation energy for an arbitrary order N of the perturbation has been presented with the aid of a circular scale of time. The method is of a recurrent character and developed for a non-degenerate quantum state. It allows one to reduce the inflation of terms necessary to calculate known from the Feynman’s diagrammatical approach to a number below that applied in the original Schrödinger perturbation Circular Scale of Time theory as a Way of Calculating the Quantum-Mechanical Perturbation Energy Given by the Schrödinger Method for potentiating the in silico discovery of molecules as efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies.

Keywords

Quantum-Mechanical Perturbation Energy, Circular Scale of Time; Circular Scale of Time; Way of Calculating the Quantum-Mechanical Perturbation Energy Given by the Schrödinger Method for potentiating the in silico designed molecules as efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies.

Abstract

The release of a quantum from a nerve terminal is accompanied by the flow of extracellular current, which creates a field around the site of transmitter action. We provide a solution for the extent of this field for the case of a quantum released from a site on an amphibian motor-nerve terminal branch onto the receptor patch of a muscle fiber and compare this with measurements of the field using three extracellular electrodes. Numerical solution of the equations for the quantal potential field in cylindrical coordinates show that the density of the field at the peak of the quantal current gives rise to a peak extracellular potential, which declines approximately as the inverse of the distance from the source at distances greater than about 4 microm from the source along the length of the fiber. The peak extracellular potential declines to 20% of its initial value in a distance of about 6 microm, both along the length of the fiber and in the circumferential direction around the fiber. Simultaneous recordings of quantal potential fields, made with three electrodes placed in a line at right angles to an FM1-43 visualized branch, gave determinations of the field strengths in accord with the numerical solutions. In addition, the three electrodes were placed so as to straddle the visualized release sites of a branch. The positions of these sites were correctly predicted on the basis of the theory and independently ascertained by FM1-43 staining of the sites. It is concluded that quantal potential fields at the neuromuscular junction that can be used on Quantal potential fields around individual amphibian motor-nerve terminal active zones for the in silico rationally designed drug Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure molecules for the possible potentiating of the efficient delivery of gene constructs through the internalization successes in experimental therapy of muscular dystrophies.

Keywords

Quantal potential fields; individual active zones; amphibian motor-nerve terminals; in silico; rationally designed; drug molecules; Peptide-mimic; pharmacologic low mass predicted; chemorecored; poly-druggable-structure; possible potentiating; efficient delivery; gene constructs; internalization successes; experimental therapy; muscular dystrophies.

Abstract

We investigate the quantum discord of a two-qubit anisotropy XXZ Heisenberg chain with Dzyaloshinskii-Moriya (DM) interaction under magnetic field. It is shown that the quantum discord highly depends on the system’s temperature T, DM interaction D, homogenous magnetic field B and the anisotropy Δ. For lower temperature T, by modulating D and B, the quantum discord can be controlled and the quantum discord switch can be utilised for the Quantum Discord of a Two-Qubit Anisotropy XXZ Heisenberg Chain with Dzyaloshinskii-Moriya Interaction of an in silico rational designed adenovirus library displaying random peptide-mimic pharmacophoric ligand supressor activities on viral naive tropism comprising replication-competent therapeutic properties for as a pancreatic cancer.

Keywords

Quantum Discords; Two-Qubit Anisotropy; XXZ Heisenberg Chain; Dzyaloshinskii-Moriya Interaction; in silico rational; designed adenovirus library; displaying random peptide-mimic; pharmacophoric ligand; supressor activities; viral naive tropism; replication-competent; therapeutic properties; pancreatic cancer;Quantum Discord, Heisenberg Chain, Dzyaloshinskii-Moriya Interaction, Anisotropy, Magnetic Field

Abstract

Cavitation bubble collapse near rough solid wall is modeled by the multi- relaxation-time (MRT) pseudopotential lattice Boltzmann (LB) model. The modified forcing scheme, which can achieve LB model’s thermodynamic consistency by tuning a parameter related with the particle interaction range, is adopted to achieve desired stability and density ratio. The bubble collapse near rough solid wall was simulated by the improved MRT pseudopotential LB model. The mechanism of bubble collapse is studied by investigating the bubble profiles, pressure field and velocity field evolution. The eroding effects of collapsing bubble are analyzed in details. It is found that the process and the effect of the interaction between bubble collapse and rough solid wall are affected seriously by the geometry of solid boundary. At the same time, in this scientific study we demonstrate that the MRT pseudopotential LB model is a potential tool for the investigation of the interaction mechanism between the surface representation Modeling for Collapsing Cavitation Bubble near Rough Solid Wall by Mulit-Relaxation-Time Pseudopotential Lattice Boltzmann Model of a designed IHMVYSK peptide-mimo based chemo-ligand comprising therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications.

Keywords

Modeling for Collapsing; Cavitation Bubble; Rough Solid Wall; Mulit-Relaxation-Time; Pseudopotential Lattice; Boltzmann Model; surface representation; IHMVYSK peptide-mimo based; chemo-ligand; therapeutic vaccine-like; agonistic properties; novel druggable; synthetic regulator; future allergic; autoimmune treatment applications; Cavitation Bubble, Bubble Collapse, Lattice Boltzmann Method, Pseudopotential Model, Rough Solid Wall.

Abstract

Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers—the numbers of connected components, holes and voids—in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis.Abstract: Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Targeting pathogenic T cells using vaccines consisting of synthetic peptides representing T cell epitopes is one such strategy that is currently being evaluated with encouraging results. Future challenges in the development of therapeutic vaccines include selection of appropriate antigens and peptides, optimization of peptide dose and route of administration and identifying strategies to induce bystander suppression. Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given peptide based on their structural complementarity. Compared to other peptide and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Peptidomimetics, deriving from structure-based, combinatorial or protein dissection approaches, can play a key role as hit compounds. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets. Here, in Biogenea we have for the first time generated Quantum representation algorithms for topological and geometric analysis of a surface designed IHMVYSK peptide-mimo based chemo-ligand comprising therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications.

Keywords

Quantum algorithms, topological, geometric, analysis, surface, representation, peptide-mimo, chemo-ligand, therapeutic, vaccine-like, agonistic, potential, druggable, synthetic, regulator, allergic, autoimmune, treatment, applications; Quantum representation algorithms; topological; geometric analysis; surface designed; IHMVYSK peptide-mimo based; chemo-ligand; therapeutic vaccine-like; agonistic properties; novel druggable; synthetic regulator ; allergic and autoimmune treatment applications.

Abstract

Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. A conditionally replicative adenovirus is a novel anticancer agent designed to replicate selectively in tumor cells. However, a leak of the virus into systemic circulation from the tumors often causes ectopic infection of various organs. Therefore, suppression of naive viral tropism and addition of tumor-targeting potential are necessary to secure patient safety and increase the therapeutic effect of an oncolytic adenovirus in the clinical setting. It has also recently been developed a direct selection method of targeted vector from a random peptide library displayed on an adenoviral fiber knob to overcome the limitation that many cell type-specific ligands for targeted adenovirus vectors are not known. In previous studies it has also been further examined whether the addition of a tumor-targeting ligand to a replication-competent adenovirus ablated for naive tropism enhances its therapeutic index. Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates. Novel methodologies are described for the analysis of multiple short linear motif like peptide structures of a protein-drug active binding conserved site. Statistical approaches that rely upon residue equivalence, but not superposition, are developed as chemogenomic informatic tasks can be performed includinig the identification of hinge regions, allosteric conformational changes and transient binding sites identified by Oncolytic virus potential Quantum algorithms for topological and geometric analysis of an in silico rational designed adenovirus library displaying random peptide-mimic pharmacophoric ligand supressor comprising viral naive tropism replication-competent pancreatic cancer therapeutic properties.

Keywords

Quantum algorithms, topological, geometric, analysis, in silico, rational, adenovirus library, displaying, random, peptide-mimic, pharmacophoric, ligand, supressor, vira,l naive, tropism, comprising, replication-competent, Oncolytic, virus, potential, therapeutic, properties pancreatic, cancer.