Abstract

In weak measurement thought experiment, an ensemble consists of M quantum particles and N states. We observe that separability of the particles is lost, and hence we have fuzzy occupation numbers for the particles in the ensemble. Without sharply measuring each particle state, quantum interferences add extra possible configurations of the ensemble, this explains the Quantum Pigeonhole Principle. This principle adds more entropy to the system; hence the particles seem to have a new kind of correlations emergent from particles not having a single, well-defined state. We formulated the Quantum Pigeonhole Principle in the language of abstract Hilbert spaces, then generalized it to systems consisting of mixed states. This insight into the fundamentals of quantum statistical mechanics could help us understand the interpretation of quantum mechanics more deeply, and possibly have implication on quantum computing and information theory. In this study, we present a multifunctional peptide based on the neutrophil immune defense molecule, CAP37, has antibacterial and wound-healing properties for Unconstrained Optimization Statistical Mechanics for Weak Measurements and Quantum Inseparability Novel procedure Computational Scaffolding on tumorigenic stem cell bacterial infected hybrids for the in silico rescaffolding and side-chain optimization on the neutrophil immune defense CAP37 protein.

Keywords

Quantum Computing, Copenhagen Interpretation, Quantum Pigeonhole Principle, Quantum Correlation, Information Theory, Quantum Statistical Mechanics, Weak Measurement, Quantum Measurement, Post Selection1, A multifunctional peptide based on the neutrophil immune defense molecule, CAP37, antibacterial; wound-healing; Unconstrained Optimization; Statistical Mechanics; Weak Measurements; Quantum Inseparability; Novel Computational Scaffolding; tumorigenic; stem cell bacterial; infected hybrids; in silico rescaffolding; side-chain; neutrophil immune; defense CAP37 protein;

Abstract

Molecular-docking programs coupled with suitable scoring functions are now established and very useful tools enabling computational chemists to rapidly screen large chemical databases and thereby to identify promising candidate compounds for further experimental processing. In a broader scenario, predicting binding affinity is one of the most critical and challenging components of computer-aided structure-based drug design. The development of a molecular docking scoring function which in principle could combine both features, namely ranking putative poses and predicting complex affinity, would be of paramount importance. Here, we systematically investigated the performance of the MM-PBSA approach, using two different Poisson–Boltzmann solvers (APBS and DelPhi), in the currently rising field of protein-peptide interactions (PPIs), identifying the correct binding conformations of 19 different protein-peptide complexes and predicting their binding free energies. First, we scored the decoy structures from HADDOCK calculation via the MM-PBSA approach in order to assess the capability of retrieving near-native poses in the best-scoring clusters and of evaluating the corresponding free energies of binding. MM-PBSA behaves well in finding the poses corresponding to the lowest binding free energy, however the built-in HADDOCK score shows a better performance. In order to improve the MM-PBSA-based scoring function, we dampened the MM-PBSA solvation and coulombic terms by 0.2, as proposed in the HADDOCK score and LIE approaches. The new dampened MM-PBSA (dMM-PBSA) outperforms the original MM-PBSA and ranks the decoys structures as the HADDOCK score does. Second, we found a good correlation between the dMM-PBSA and HADDOCK scores for the near-native clusters of each system and the experimental binding energies, respectively. Therefore, we propose a new scoring function, dMM-PBSA, to be used together with the built-in HADDOCK score in the context of protein-peptide docking HADDOCK scoring Function simulations for Protein-Peptide Docking on a SVS-1 multipharmacophore as a potential drug-like efficator in Preceding Membrane Neutralization using a multi-mimotopic algorithmic approach for biclustering analysis of expression data.

Keywords

dMM-PBSA, HADDOCK, Scoring Function, Protein-Peptide, Docking, In silico, Anticancer Peptide, SVS-1 multipharmacophore, drug-like, efficator, Preceding Membrane Neutralization, web server, multi-mimotopic, algorithmic approach, biclustering analysis, expression data

Abstract

Anticancer peptides (ACPs) are polycationic amphiphiles capable of preferentially killing a widespectrum of cancer cells relative to non-cancerous cells. Their primary mode of action is aninteraction with the cell membrane and subsequent activation of lytic effects, however it remainscontroversial the exact mechanism responsible for this mode of action. It has in previous studies been shown that utilizing zeta potential analyses it was possible to demonstrate the interaction of a small anticancer peptide with membrane modelsystems and cancer cells. Electrostatic interactions have a pivotal role in the cell killing processand in contrast to the AMPs action cell death occurs without achieving full neutralization of themembrane charge. The advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Web-enabled service called GEMS (Gene Expression Mining Server) for biclustering microarray data where Users may upload expression data and specify a set of criteria.GEMS performs bicluster mining based on a Gibbs sampling paradigm. Here, in Biogenea we have for the first time discovered an Anticancer Peptide SVS-1 multipharmacophore with an ini silico evaluated Efficacy in Preceding Membrane Neutralization using a web server for biclustering analysis of expression data. Quantum information processing devices need to be robust and stable against external noise and internal imperfections to ensure correct operation. In a setting of measurement-based quantum computation, we explore how an intelligent agent endowed with a projective simulator can act as controller to adapt measurement directions to an external stray field of unknown magnitude in a fixed direction. We assess the agent’s learning behavior in static and time-varying fields and explore composition strategies in the projective simulator to improve the agent’s performance. We demonstrate the applicability by correcting for stray fields in a measurement-based algorithm for Grover’s search. Thereby, we lay out a path for adaptive controllers based on intelligent agents for quantum information tasks.
When building devices for quantum information processing one has to take changing environment conditions and device imperfections into account. It is therefore necessary to include adaptive mechanisms that characterize and calibrate the device from within. Furthermore, it is desirable for these devices to obtain a certain degree of autonomy in maintaining their functional state despite detrimental environment influences, in particular, when they are assembled to a larger quantum information processing infrastructure. In the attempt to miniaturize current implementations of quantum devices, we will reach the point where these devices will be of microscopic scale and require short reaction times. For such microscopic systems we can no longer assume that their internal controllers are full-fledged universal computers that can carry out arbitrary programs. Instead, controllers will be small physical systems that are specialized for their respective purpose with a program that emerges from the controller’s analog dynamics. In this paper we explore the applicability of a controller in form of an intelligent learning agent that has access to a projective adaptive quantum computated simulator in changing environments using a multi-mimotopic algorithmic approach for biclustering analysis of expression data projective simulation on an Anticancer Peptide SVS-1 multipharmacophore as a potential drug-like efficator in Preceding Membrane Neutralization.

Keywords

Adaptive quantum computation; environments; projective simulation;In silico; Anticancer Peptide; SVS-1; multipharmacophore; drug-like; efficator; Preceding Membrane; Neutralization; web server; multi-mimotopic; algorithmic; biclustering analysis; expression data;

Abstract

The main facts about the scale of time considered as a plot of a sequence of events are submitted both to a review and a more detailed calculation. Classical progressive character of the time variable, present in the everyday life and in the modern science, too, is compared with a circular-like kind of advancement of time. This second kind of the time behaviour can be found suitable when a perturbation process of a quantum-mechanical system is examined. In fact the paper demonstrates that the complicated high-order Schrödinger perturbation energy of a non-degenerate quantum state becomes easy to approach of the basis of a circular scale. For example for the perturbation order N = 20 instead of 19! ≈ 1.216 × 1017 Feynman diagrams, the contribution of which should be derived and calculated, only less than 218 ≈ 2.621 × 105 terms belonging to N = 20 should be taken into account to the same purpose. Diabetes mellitus affects over 100 million individuals worldwide. In the U.S., the estimated healthcare costs of those affected by diabetes is approximately 136 billion dollars annually. Diabetes mellitus is a disorder of the metabolism that is characterized by the inability of the pancreas to secrete sufficient amounts of insulin, which results in large fluctuations in blood glucose levels and can have both short- and long-term physiological consequences. Glucagon-like peptide-1 (7-36) amide (GLP-1) is a gut hormone, released postprandially,which stimulates insulin secretion and insulin gene expression as well as pancreatic B-cell growth. Together with glucose-dependent insulinotropic polypeptide (GIP), it is responsible for the incretin effect which is the augmentation of insulin secretion following oral administration of glucose. We therefore for the first time provided in this scientific project a promising alternative to bridge the knowledge gap between insulinotropic biological conserved signaling pathways and chemistry informatic tools which significantly boost the productivity of our chemogenomics research for the Circular Scale of Time and Energy of a Quantum State Calculated from the Schrödinger Perturbation Theory on computational target fishing mining machinery based chemogenomic databases as an Identification tool for predicting therapeutic potential of a high-potency GLP-1, INGAP-P/IGLHDPSHGTLPNGS peptide mimetic insulinotropic compounds.

Keywords

computational, target fishing, mining machinery;novel target Identification tool;predicting therapeutic potential;proinsulin; GLP-1; INGAP-Ppeptide; mimetic;insulinotropic; compounds;chemogenomic database; Circular Scale; Time and Energy; Quantum State, Calculated; Schrödinger Perturbation Theory; A computational target fishing mining machinery as an Identification tool for predicting therapeutic potential of GLP-1, INGAP-P and IGLHDPSHGTLPNGS peptide mimetic insulinotropic of high-potency compounds based on chemogenomic databases Non-Degenerate Quantum State;

Abstract

A process model of quantum mechanics utilizes a combinatorial game to generate a discrete and finite causal space, which can be defined as a self-consistent quantum mechanics. An emergent space-time and continuous wave function arise through a non-uniform interpolation process. Standard non-relativistic quantum mechanics emerges under the limit of infinite information (the causal space grows to infinity) and infinitesimal scale (the separation between points goes to zero). This model has the potential to address a computational target fishing mining machinery Process Model of Quantum Mechanics Circular Scale of Time and Energy of a Quantum State Calculated from the Schrödinger Perturbation Theory as an Identification tool for predicting therapeutic potential of GLP-1, INGAP-P/IGLHDPSHGTLPNGS peptide mimetic insulinotropic of high-potency compounds.

Keywords

Process Theory; Quantum Foundations; Discrete Models; Emergent Models; Process Model; Quantum Mechanics; Circular Scale; Time and Energy; Quantum State Calculated; Schrödinger Perturbation Theory; computational target fishing; mining machinery; Identification tool; predicting therapeutic potential; GLP-1, INGAP-P; IGLHDPSHGTLPNGS peptide mimetic; insulinotropic; high-potency; compounds; chemogenomic databases;

Abstract

p38a is a significant target for drug designing against cancer. The overproduction of p38a MAPK promotes tumorigenesis inhead and neck squamous cell carcinoma (HNSCC). The ATP binding and an allosteric site referred as DFG are the key sites ofthe p38a mitogen activated protein kinase (MAPK) exploited for the design of inhibitors. The p38 mitogen-activated protein kinase (MAPK) is a signaling intermediate downstream of proinflammatory cytokine receptors released following environmental stress. This kinase is known to play an important role in inflammatory and autoimmune diseases, including rheumatoid arthritis and multiple sclerosis. Here, we present for the first time a novel drug-target combined Bayesian formulation for the Rational Discovery of a Specific FWCS/LQIHFTLIXAFCCEN Peptide mimetic Pharmacophoric Inhibitor against p38a MAPK at Allosteric-Sites-HNSCC. A Therapeutic Modality forHNSCC using Predicting drug–target interactions from chemical and genomic kernels by Bayesian matrix factorization. This paper discusses quantum mechanical schemas for describing waves with non-abelian phases, Fock spaces of annihilation-creation operators for these structures, and the Feynman recipe for obtaining descriptions of particle interactions with external novel drug-target fields combined to Bayesian formulation for the Rational Discovery of a Specific FWCS/LQIHFTLIXAFCCEN Peptide mimetic Pharmacophoric Inhibitor against p38a MAPK at HNSCC Allosteric-Sites on Hypercomplex based Extensions of Quantum Theory.

Keywords

novel; drug-target;combined; Bayesian formulation;Rational Discovery; FWCS/LQIHFTLIXAFCCEN;Peptide mimetic;Pharmacophoric Inhibitor;p38a MAPK; Allosteric-Sites;HNSCC; A novel drug-target combined Bayesian formulation for the Rational Discovery of a Specific; Hypercomplex Extensions; Quantum Theory; novel drug-target; Bayesian formulation; Allosteric-Sites; Composition Algebras; Hilbert Spaces; Fock Spaces; Non-Abelian Gauge Fields;

Abstract

Using von Neumann’s continuous geometry in conjunction with A. Connes’ noncommutative geometry an exact mathematical-topological picture of quantum spacetime is developed ab initio. The final result coincides with the general conclusion of E-infinity theory and previous results obtained in the realm of high energy physics. In particular it is concluded that the quantum particle and the quantum wave spans quantum spacetime and conversely quantum particles and waves mutates from quantum spacetime of a novel drug-target combined Bayesian formulation for the Rational Discovery of a Specific FWCS/LQIHFTLIXAFCCEN Peptide mimetic Pharmacophoric Inhibitor against p38a MAPK at HNSCC Allosteric-Sites.

Keywords

E-Infinity, Quantum Spacetime, Noncommutative Geometry, Fractals, Transfinite Set Theory, Von Neumann Continuous Geometry, Cantor Sets, Fusion Algebra, Zero Point Energy, Vacuum Fluctuation, Quantum Field Theory, Casimir Effect, Dark Energy, Exact Mathematical Picture; Quantum Spacetime;Hypercomplex Extensions; Quantum Theory; novel drug-target; combined Bayesian formulation; Rational Discovery; FWCS/LQIHFTLIXAFCCEN; Peptide mimetic; Pharmacophoric Inhibitor; p38a MAPK; HNSCC Allosteric-Sites;

Abstract

In previous scientific efforts random peptide libraries containing millions of 90 Mm TentaGel beads, each withits own unique ovarian cancer disease associated amino acid sequence were generated by using ‘‘one-bead one-compound’’ combinatorial chemistry tchnology. A cyclic random 8-mer library was screened with CAOV-3 (a human ovarian adenocarcinoma cell line) and beads with a unique ligandthat bind to the cell surface receptors were coated byone or more layers of cells. These positive beads were isolated, stripped, and microsequenced. Several peptidemotifs were identified from these screenings, some of which were novel and unique, e.g., cDGX4GX6X7c. Structure-activity relationship studies of this peptiderevealed that the L-aspartate residue at position 2, thetwo glycines at positions 3 and 5, and the two Dcysteinesat the amino and COOH terminus are critical foractivity. Here, in Biogenea we have for the first time in silico discovered novel targeting peptide chemical pharmacophore for human ovarian cancer highly expressed mRNAs fro ‘‘one-bead one-compound’’combinatorial libraries.

Keywords

hydrophobic; residue;computer-aided; discovery;novel targeting; peptide;chemical; pharmacophore;human ovarian cancer; highly expressed; mRNAs, ‘‘one-bead one-compound’’combinatorial libraries;

Abstract

The existence of any elementary particle in universe requires the existence of some region of universe occupied by it. By taking the volume of this occupied region, the author will reformulate the relativistic quantum field theory using new 3-dimensional region-like idealization of elementary particles and hereinafter will call the total volume of all regions occupied by the elementary constituent particles of the quantum system the occupied volume. Also the author will call the set of all regions of universe filled by elementary constituent particles of the quantum system the occupied path. Always any quantum system is existed at a head of its occupied path. This path is growing by mutual filling and leaving regions of universe by its elementary constituent particles. The conservation of this elementary constituent particle requires the conservation of its occupied volume during this process. This requirement could be summarized by the following conditions: 1) the total volume of all regions of universe filled by the elementary constituent particles of the quantum system minus the total volume of all regions of universe left by these elementary constituent particles must be equal to the occupied volume of the quantum system; 2) the total increase in the occupied volume of the quantum system due to the absorption of another elementary particles from outside its occupied regions minus the total decreasing in its occupied volume due to the emission of another elementary particles outside its occupied regions must be equal to the occupied volume of it. The wave-particle duality of the elementary constituent particles implied accumulation of them as the finite set of interfered waves. This accumulation of elementary constituent particles causes the absolute probabilistic nature of event of finding the elementary consistent particle in specified interfered wave, and hence the mathematical representation of this interfered wave should take into account the value of probability amplitude of finding an elementary particle inside the region occupied specified interfered wave. In quantum theory this probability amplitude corresponds to complex amplitude of the wave function of interfered wave. Also in Hilbert’s representation of the quantum theory these wave functions are representing the components of the quantum state vector. In this paper the author will develop the transformation theory of the region-like quantum state of the quantum system. In this study, we managed to reformulate of the Relativistic Quantum Field Theory using Region-Like Idealization of the Elementary Particle Identification of an one-bead by one-compound computer-aided discovery novel peptide mimetic pharmacophore against human ovarian cancer highly expressed on conserved mRNAs-cDGX4GX6X7c associated combinatorial libraries.

Keywords

Reformulation; Relativistic Quantum Field Theory; Region-Like Idealization; Elementary Particle Identification; one-bead by one-compound; computer-aided discovery; novel peptide mimetic; pharmacophore; human ovarian cancer; highly expressed; fconserved; mRNAs-cDGX4GX6X7c; combinatorial libraries; Creation, Annihilation, Animation, Occupied Volume, Occupied Path,

Abstract

BACE-1 is the β-secretase responsible for the initial amyloidogenesis in Alzheimer’s disease, catalyzing hydrolytic cleavage of substrate in a pH-sensitive manner. The catalytic mechanism of BACE-1 requires water-mediated proton transfer from aspartyl dyad to the substrate, as well as structural flexibility in the flap region. Thus, the coupling of protonation and conformational equilibria is essential to a full in silico characterization of BACE-1. In this work, we perform constant pH replica exchange molecular dynamics simulations on both apo BACE-1 and five BACE-1-inhibitor complexes to examine the effect of pH on dynamics and inhibitor binding properties of BACE-1. In our simulations, we find that solution pH controls the conformational flexibility of apo BACE-1, whereas bound inhibitors largely limit the motions of the holo enzyme at all levels of pH. The microscopic pKa values of titratable residues in BACE-1 including its aspartyl dyad are computed and compared between apo and inhibitor-bound states. Changes in protonation between the apo and holo forms suggest a thermodynamic linkage between binding of inhibitors and protons localized at the dyad. Utilizing our recently developed computational protocol applying the binding polynomial formalism to the constant pH molecular dynamics (CpHMD) framework, we are able to obtain the pH-dependent binding free energy profiles for various BACE-1-inhibitor complexes. Our results highlight the importance of correctly addressing the binding-induced protonation changes in protein-ligand systems where binding accompanies a net proton transfer. This work comprises the first application of our CpHMD-based free energy computational method to protein-ligand complexes and illustrates the value of CpHMD as an all-purpose tool for obtaining pH-dependent dynamics and binding free energies of biological systems.In silico designed of an Anticancer Peptide SVS-1 multipharmacophore as a potential drug-like efficator in Preceding Membrane Neutralization using a web server multi-mimotopic algorithmic approach for biclustering analysis of expression data.Conformational Dynamics and Binding Free Energies of Inhibitors of BACE-1: From the Perspective of Protonation Equilibria. Anticancer peptides (ACPs) are polycationic amphiphiles capable of preferentially killing a widespectrum of cancer cells relative to non-cancerous cells. Their primary mode of action is aninteraction with the cell membrane and subsequent activation of lytic effects, however it remainscontroversial the exact mechanism responsible for this mode of action. It has in previous studies been shown that utilizing zeta potential analyses it was possible to demonstrate the interaction of a small anticancer peptide with membrane modelsystems and cancer cells. Electrostatic interactions have a pivotal role in the cell killing processand in contrast to the AMPs action cell death occurs without achieving full neutralization of themembrane charge. Formation of insoluble amyloid plaques in the vascular and hippocampal areas of the brain characterizes Alzheimer’s disease, a devastating neurodegenerative disorder causing dementia. Site-specific hydrolytic catalysis of β-secretase, or BACE-1, is responsible for production of oligomerative amyloid β-peptide. As the catalytic activity of BACE-1 is pH-dependent and its structural dynamics are intrinsic to the catalysis, we examine the dependence of dynamics of BACE-1 on solution pH and its implications on the catalytic mechanism of BACE-1. Also, we highlight the importance of accurate description of protonation states of the titratable groups in computer-aided drug discovery targeting BACE-1. We hope the understanding of pH dependence of the dynamics and inhibitor binding properties of BACE-1 will aid the structure-based inhibitor design efforts against Alzheimer’s disease.In silico designed of an Anticancer Peptide SVS-1 multipharmacophore as a potential drug-like efficator in Preceding Membrane Neutralization using a web server multi-mimotopic algorithmic approach for biclustering analysis of expression data.Conformational Dynamics and Binding Free Energies of Inhibitors of BACE-1: From the Perspective of Protonation Equilibria. The advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Web-enabled service called GEMS (Gene Expression Mining Server) for biclustering microarray data where Users may upload expression data and specify a set of criteria. In this study GEMS performs bicluster mining based on a Gibbs sampling paradigm of Conformational Dynamics and Binding Free Energies of Inhibitors of BACE-1: From the Perspective of Protonation Equilibria to the in silico design of an Anticancer Peptide SVS-1 multipharmacophore as a potential drug-like efficator in Preceding Membrane Neutralization using a multi-mimotopic algorithmic approach for biclustering analysis of expression data.. Here, in Biogenea we have for the first time discovered an Anticancer Peptide SVS-1 multipharmacophore with an ini silico evaluated Efficacy in Preceding Membrane Neutralization using a web server for biclustering analysis of expression data.

Keywords

In silico designed; Anticancer Peptide; SVS-1; multipharmacophore; potential; drug-like; efficator; Preceding Membrane; Neutralization; multi-mimotopic; algorithmic approach; biclustering analysis; expression data; Conformational Dynamics; Binding Free Energies; Inhibitors; BACE-1; Protonation Equilibria;