Abstract

We discuss the fact that there is a crucial contradiction within Von Neumann’s theory. We derive a proposition concerning a quantum expected value under an assumption of the existence of the orientation of reference frames in N spin-1/2 systems (1 ≤ N < +∞). This assumption intuitively depictures our physical world. However, the quantum predictions within the formalism of Von Neumann’s projective measurement violate the proposition with a magnitude that grows exponentially with the number of particles. We have to give up either the existence of the directions or the formalism of Von Neumann’s projective measurement. Therefore, Von Neumann’s theory cannot depicture our physical world with a violation factor that grows exponentially with the number of particles. The theoretical formalism of the implementation of the Deutsch-Jozsa algorithm relies on Von Neumann’s theory. We investigate whether Von Neumann’s theory meets the Deutsch-Jozsa algorithm. We discuss the fact that the crucial contradiction makes the quantum-theoretical formulation of Deutsch-Jozsa algorithm questionable. Further, we discuss the fact that projective measurement theory does not meet an easy detector model for a single Pauli observable. Especially, we systematically describe our assertion based on more mathematical analysis using raw data. We propose a solution of the problem. to changing Planck’s constant (h) to a new constant. It may be said that a new type of the quantum theory early approaches Newton’s theory in the macroscopic scale than the old quantum theory does. We discuss how our solution is used in an implementation of Deutsch’s algorithm, Von Neumann’s Theory, Projective Measurement, and Quantum Computation data generations of a multifunctional peptide-mimic chemo-structure by connecting conserved fragments based on the neutrophil immune defense CAP37 protein as an in-silico antibacterial and wound-healing canditate agent.

Keywords

Von Neumann’s Theory, Projective Measurement, Quantum Computation data; multifunctional; peptide-mimic; chemo-structure; connecting conserved fragments; neutrophil immune defense; CAP37 protein; in-silico; antibacterial; wound-healing; canditate agent, Quantum Computation, Quantum Measurement Theory, Formalism;

Abstract

Hydrocarbon stapling can restore bioactive α-helical structure to natural peptides, yielding research tools and prototype therapeutics to dissect and target protein interactions. The capacity of peptide stapling to generate high-fidelity, protease-resistant mimics of antigenic structures for vaccine development has been previoulsy explored. HIV-1 has been refractory to vaccine technologies thus far, although select human antibodies can broadly neutralize HIV-1 by targeting sequences of the gp41 juxtamembrane fusion apparatus. Candidate HIV-1 immunogens, have been generated and characterized stabilized α-helices of the membrane-proximal external region (SAH-MPER) of gp41 have been utilized. SAH-MPER peptides were remarkably protease resistant and bound to the broadly neutralizing 4E10 and 10E8 antibodies with high affinity, recapitulating the structure of the MPER epitope when differentially engaged by the two anti-HIV Fabs. Here, we discovered for the first time the GENEA-StacHIVenar-10085 utilising a computer aided generation of a prototype superset Stapled HIV-1 peptide-similar full-match pharmacophoric poly-agent recapitulating antigenic viral replication structures bounded broadly targeted to the neutralizing 4E10 and 10E8 antibodies.

Keywords

computer-aided; predicted;Stapled-HIV-1;peptide-mimic;pharmacophoric-poly-agent;
recapitulating-antigenic;structures;computer aided; generation; prototype; superset; Stapled HIV-1; peptide-similar; full-match; pharmacophoric; poly-agent; recapitulating antigenic; viral replication; structures; bounded broadly; neutralizing 4E10 and 10E8 antibodies;

Abstract

Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. For quantum systems, where the physical dimension grows exponentially, finding the eigenvalues of certain operators is one such intractable problem and remains a fundamental challenge. The quantum phase estimation algorithm efficiently finds the eigenvalue of a given eigenvector but requires fully coherent evolution. Here we present an alternative approach that greatly reduces the requirements for coherent evolution and combine this method with a new approach to state preparation based on ansätze and classical optimization. We implement the algorithm by combining a highly reconfigurable photonic quantum processor with a conventional computer. We experimentally demonstrate the feasibility of this approach with an A variational eigenvalue solver rational design of a computer-aided poly-pharmacophore on a photonic quantum processor synthetic molecule comprising therapeutic peptide-mimic superagonistic properties of 829,16kcal.mol against to Ebola virus conserved protein regions from quantum chemistry—calculating the ground-state molecular energy for He–H+. The proposed approach drastically reduces the coherence time requirements, enhancing the potential of quantum resources available today and in the near future.

Keywords

A variational eigenvalue solver on a photonic quantum processor Rational design of a computer-aided poly-pharmacophore synthetic molecule comprising therapeutic peptide-mimic superagonistic properties of 829,16kcal.mol against to Ebola virus conserved protein regions.A variational eigenvalue solver on a photonic quantum processor.

Abstract

The molecular mechanics energies combined with the Poisson–Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods are popular approaches to estimate the free energy of the binding of small ligands to biological macromolecules. They are typically based on molecular dynamics simulations of the receptor–ligand complex and are therefore intermediate in both accuracy and computational effort between empirical scoring and strict alchemical perturbation methods. They have been applied to a large number of systems with varying success. Antimicrobial peptides (AMPs) which predominantly act via membrane active mechanisms have emerged as an exciting class of antimicrobial agents with tremendous potential to overcome the global epidemic of antibiotics-resistant infections. The first generation of AMPs derived from natural sources as diverse as plants, insects and humans has provided a wealth of compositional and structural information to design novel synthetic AMPs with enhanced antimicrobial potencies and selectivities, reduced cost of production due to shorter sequences and improved stabilities under physiological conditions. As a rational result we discovered for the first time the GENEA-Antimamphiler-109 utilizing Rational Elaborated Common Strategies employed MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities for the efficient in silico optimization of an accesible synthetically (AMPs) peptidomimetic-similar to an amphiphile-based pharmacophoric agent as a promising enhanced therapeutic antimicrobial agent.

Keywords

MM/PBSA;MM/GBSA; methods; ligand-binding affinities; Rational Elaborated; Common Strategies; in silico; optimization; accesible; synthetically; (AMPs) peptidomimetic; amphiphile-based; pharmacophoric agent; therapeutic antimicrobial agent;

Abstract

In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known de novo ligand Identification Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets of a structural ligand-based synthetically accesible pharmacophoric determinant on tau protein-mimic conserved motif peptide chemical elements as an annotated promising therapy in drug discovery approaches and proposes alternative approaches for increasing efficiency against Alzheimer’s disease treatment.

Keywords

drug discovery, drug design, drug targets, repositioning, molecular imaging, Complementary Approaches; Existing; Target Based; Drug Discovery; Identifying; Novel Drug Targets; De novo ligand; Identification;structural; ligand-based; synthetically accesible; pharmacophoric determinant ; tau protein-mimic; conserved motif; peptide chemical; elements; annotated; promising in Alzheimer’s disease;

Abstract

In this work the quantum chemistry Tersoff potential in combination with classical trajectory calculations was used to investigate the interaction of the DNA molecule with a carbon nanotube (CNT). The so-called hybrid approach—the classical and quantum-chemical modeling, where the force fields and interaction between particles are based on a definite (but not unique) description method, has been outlined in some detail. In such approach the molecules are described as a set of spheres and springs, thereby the spheres imitate classical particles and the spring the interaction force fields provided by quantum chemistry laws. The Tersoff potential in hybrid molecular dynamics (MD) simulations correctly describes the nature of covalent bonding. The aim of the present work was to estimate the dynamical and structural behavior of the DNA-CNT system at ambient temperature conditions. The dynamical configurations were built up for the DNA molecule interacting with the CNT. The analysis of generated МD configurations for the DNA-CNT complex was carried out. For the DNA-CNT system the observations reveal an encapsulation-like behavior of the DNA chain inside the CNT chain. The discussions were made on possible use of the DNA-CNT complex as a candidate material in drug delivery and related systems. Cardiac cell therapy has been proposed as one of the new strategies against myocardial infarction. Although several reports showed improvement of the function of ischemic heart, the effects of cell therapy vary among the studies and the mechanisms of the beneficial effects are still unknown. Previously, it has been reported that clonal stem cell antigen-1-positive cardiac progenitor cells exerted a therapeutic effect when transplanted into the ischemic heart. Considerable efforts have been achieved to identify the cardiac progenitor-specific paracrine factor and to elucidate the mechanism of its beneficial effect. Basic concepts and applications of data science to the genetic analysis of pharmacologic outcomes have also in the past presented. Drug repositioning is a challenging computational problem involving the integration of heterogeneous sources of biomolecular data and the design of label ranking algorithms able to exploit the overall topology of the underlying pharmacological netResearch. As a result we for the first time generated aMeta-Dynamic Meta-node Hybrid Quantum Chemistry Potential and Classical Trajectory Molecular Dynamics Simulations of the DNA-CNT Interaction reconsrtructing approach for the in silico generation of a drug-construct consisting of annotated Anti-inflammatory anti-(JAM-A) peptide-mimic pharmacophores with a potential myocardial infarction therapeutic activity.

Keywords

Meta-Dynamic; Meta-node; reconsrtructing approach; in silico; stochastic generation;novel; drug-construct; novel in-silico;drug-designmethodology pharmacophoric generation Anti-inflammatory (JAM-A) peptide-mimic conformational complexity pharmacophores Hybrid Quantum Chemistry Potential and Classical Trajectory Approach, Molecular Dynamics; Carbon Nanotube; DNA Molecule; Drug Delivery; DNA-CNT Interaction

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 realized. Wilms’ Tumour 1 (WT1) is a zinc finger transcription factor that is overexpressed in acute myeloid leukaemia (AML). Its restricted expression in normal tissues makes it a promising target for novel immunotherapies aiming to accentuate the cytotoxic T lymphocyte (CTL) response against AML. It has been previously reported a phase I/II clinical trial of subcutaneous peptide vaccination with two separate HLA-A2-binding peptide epitopes derived from WT1, together with a pan-DR binding peptide epitope (PADRE), in Montanide adjuvant. Here, in Biogenea we have for the first time perfermed Quantum Discord of an in silico Interaction designed Two-Qubit Anisotropy XXZ Heisenberg Chain with Dzyaloshinskii-Moriya Fusion Inhibitor consisting of five cancer filtered conserved pharmacophoric chemical fragments with Greatly Promising Pharmaco-Mimic Properties to a Rationally Engineered Wilms’ Tumor Peptide as a future computer generated hyper-molecule for the potential treatment of the acute myeloid leukemia.

Keywords

Quantum Discord; Two-Qubit;Anisotropy XXZ Heisenberg Chain; Dzyaloshinskii-Moriya; in silico; Fusion Inhibitor; five cancer; filtered; conserved; pharmacophoric; chemical fragments; Greatly Promising; Pharmaco-Mimic Properties; Rationally Engineered; Wilms’ Tumor Peptide; future; computer; generated; hyper-molecule; potential treatment; acute myeloid leukemia, Quantum Discord, Heisenberg Chain, Dzyaloshinskii-Moriya Interaction, Anisotropy, Magnetic Field;

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 realized. The insulin-like growth factor-I (IGF-I) is a key regulator of skeletal muscle growth in vertebrates, promoting mitogenic and anabolic effects through the activation of the MAPK/ERK and the PI3K/Akt signaling pathways. Also, these results show that there is a time-dependent regulation of IGF-I plasma levels and its signaling pathways in muscle. The insulin-like growth factor-I (IGF-I) is a key regulatory hormone that controls growth in vertebrates. Particularly, skeletal muscle growth is strongly stimulated by this hormone. IGFI stimulates both proliferation and differentiation of myoblasts, as well as promoting myotube hypertrophy in vitro and in vivo. The mitogenic and anabolic effects of IGF-I on muscle cells are mediated through specific binding with the IGF-I receptor (IGF-IR). This ligand-receptor interaction promotes the activation of two major intracellular signaling pathways, the mitogen-activated protein kinases (MAPKs), specifically the extracellular signal-regulated kinase (ERK), and the phosphatidylinositol 3 kinase (PI3K)/Akt. The MAPK (RAF/MEK/ERK) is a key signaling pathway in skeletal muscle, where its activation is absolutely indispensable for muscle cell proliferation. Biologically active polypeptides derived from the E domain that forms the C-terminus of the insulin-like growth factor I (IGF-I) splice variant known as mechano growth factor which have been demonstrated neuroprotective and cardioprotective properties, as well as the ability to increase the strength of normal and dystrophic skeletal muscle. Ligands selected from phage-displayed random peptide libraries tend to be directed to biologically relevant sites on the surface of the target protein. Protein-peptide interactions form the basis of many cellular processes. Consequently, peptides derived from library screenings often modulate the target protein’s activity in vitro and in vivo and can be used as lead compounds in drug design and as alternatives to antibodies for target validation in both genomics and drug discovery. In this research and science project we for the first time presented Quantum Discord of a Two-Qubit Anisotropy XXZ Heisenberg Chain with a Dzyaloshinskii-Moriya predicted Interaction analysis for the discovery of a chemo-polypharmacophoric agent comprising (Propeptide-Fc)/MGF peptide mimicking interactive of high free binding energy properties towards Wnt7a/Fzd7 signalling Akt/mTOR anabolic growth IGF-I/PI3K/Akt -I/MAPK/ERK pathways.

Keywords

Quantum Discord; Two-Qubit; Anisotropy XXZ; Heisenberg Chain; Dzyaloshinskii-Moriya; predicted Interaction; chemo-polypharmacophoric; agent; (Propeptide-Fc)/MGF peptide; mimicking interactive; high free binding energy; Wnt7a/Fzd7 signalling Akt/mTOR; anabolic growth; IGF-I/PI3K/Akt -I/MAPK/ERK pathways, Quantum Discord, Heisenberg Chain, Dzyaloshinskii-Moriya Interaction, Anisotropy, Magnetic Field;

Abstract

Abstract

Human society is currently generating on the order of Avogadro’s number (6 × 1023) of bits of data a year. Extracting useful information from even a small subset of such a huge data set is difficult. A wide variety of big data processing techniques have been developed to extract from large data sets the hidden information in which one is actually interested. Topological techniques for analysing big data represent a sophisticated and powerful tool1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24. By its very nature, topology reveals features of the data that robust to how the data were sampled, how it was represented and how it was corrupted by noise. Persistent homology is a particularly useful topological technique that analyses the data to extract topological features such as the number of connected components, holes, voids and so on (Betti numbers) of the underlying structure from which the data was generated. The length scale of analysis is then varied to see whether those topological features persist at different scales. A topological feature that persists over many length scales can be identified with a ‘true’ feature of the underlying structure. 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. Stimulating an immune response against cancer with the use of vaccines remainsa challenge. We hypothesized that combining a melanoma vaccine with interleukin-2, an immuneactivating agent, could improve outcomes. In a previous phase 2 Research Scientific Project, patients with metastaticmelanoma receiving high-dose interleukin-2 plus the gp100:209–217(210M) peptide vaccine hada higher rate of response than the rate that is expected among patients who are treated withinterleukin-2 alone. We here, present an evolutionary algorithm that works in conjunction with existing open-source software to automatically optimize candidate ligands for predicted binding affinity and other druglike properties. We used the rules of click chemistry to guide optimization, greatly enhancing synthesizability. Here, we have for the first time generated an Improved data computer Quantum Algorithm for Chemically Tractable, Semi-Automated topological and geometric Protein Inhibitor Design analysis simulated of a gp100 Peptide mimic pharmacophore as a Vaccine-like and Interleukin-2 in silico generated superagonist with potential clinical effect in Patients with Advanced Melanoma.

Keywords

Quantum algorithms; topological; geometric analysis; data; computer simulated; gp100 Peptide mimic; designed pharmacophore; Vaccine-like; Interleukin-2; in silico; superagonist; potential; clinical effect; Patients with Advanced Melanoma; Improved Algorithm; Chemically Tractable, Semi-Automated; Protein Inhibitor Design;