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;

Abstract

The hypothesis suggesting that the physical process of quantum tunneling can be used as a form of cancer therapy in electron ionization radiotherapy was suggested in the IEEE International Conference on Electric Information and Control Engineering by G. Giovannetti-Singh (2012) [1]. The hypothesis used quantum wave functions and probability amplitudes to find probabilities of electrons tunneling into a cancer cell. In addition, the paper explained the feasibilities of the therapy, with the use of nanomagnets. In this paper, we calculate accurate probability densities for the electron beams to tunnel into cancer cells. We present our results of mathematical modeling based on the helical electron wave function, which “tunnel” into a cancer cell, therefore ionizing it more effectively than in conventional forms of radiotherapy. We discuss the advantages of the therapy, and we explain how quantum mechanics can be used to create new cancer therapies, in particular our suggested an Improved Mathematical data computer simulated Modeling for Quantum Electron Wave Mechanichs Algorithm for Chemically Tractable, Semi-Automated Protein Inhibitor Design of a gp100 Peptide mimic designed pharmacophore as a Vaccine-like and Interleukin-2 in silico generated superagonist with potential clinical effect in Patients with Advanced Melanoma.

Keywords

Electron Wave Therapy; Quantum Tunneling; Wave Function; Quantum Theory; Cancer Therapy, Mathematical Modeling for Quantum Electron Wave Therapy data computer simulated on a gp100 Peptide mimic designed pharmacophore as a Vaccine-like and Interleukin-2 in silico generated superagonist with potential clinical effect in Patients with Advanced Melanoma using an Improved Algorithm for Chemically Tractable, Semi-Automated Protein Inhibitor Design.

Abstract

This paper describes an integrated platform for machine learning and big data analysis. The integrated platform is configured in a way that builds a large distributed data processing environment in the computing environment that makes up the NVIDIA AI platform. In addition, this paper describes the background of this idea selection and the use of the software to build the unified platform. The technical details are shown in terms of how to create the proposed platform. In the anlaysis section, the methodology is provided and also the steps are explained as to how to use this integration platform. Finally, the expected effects are elaborated in the conclusion section.Keywords:Integrated Platform, Hadoop Eco System, Ambari, Virtual OS, Jetson TX-1, Dev Box, SSH1. Regeneration of the central nervous system presents a formidable challenge within regenerative medicine, as neurons in the brain and spinal cord have very limited potential for healing and reorganization. The Ile-Lys-Val-Ala-Val (IKVAV) peptide sequence, derived from laminin, has been incorporated into PAs for applications in neural regeneration in order to enhance neural attachment, migration, and neurite outgrowth. Variations in peptide sequence, while maintaining the alternating ionic hydrophilic and hydrophobic residues, have utilized mixed charged residues, such as repeat units of Arg-Ala-Asp-Ala (RADA) or repeat units of RARADADA. Although docking and scoring relies on many approximations, the application of our clustering techniques during lead optimization, with other computational methods, extended more traditional approaches to a Unified Platform for AI and Big Data Analytics Logical computations using algorithmic self-assembly RGD-FHRRIKA-RARADADA-IKVAV responsive peptide-modified mimetic triple-crossover hydrothermochemic molecules for tissue regeneration.

Keywords

Logical; computations;algorithmic; self-assembly;peptide-modified mimetic;triple-crossover; hydrothermochemic; molecules;tissue; regeneration;Unified Platform; AI Big Data Analytics; Logical computations; algorithmic; self-assembly; RGD-FHRRIKA-RARADADA-IKVAV; responsive; peptide-modified; mimetic; triple-crossover; hydrothermochemic; molecules; tissue regeneration;

Abstract

This paper describes an integrated platform for machine learning and big data analysis. The integrated platform is configured in a way that builds a large distributed data processing environment in the computing environment that makes up the NVIDIA AI platform. In addition, this paper describes the background of this idea selection and the use of the software to build the unified platform. The technical details are shown in terms of how to create the proposed platform. In the anlaysis section, the methodology is provided and also the steps are explained as to how to use this integration platform. Finally, the expected effects are elaborated in the Unified Platform for AI and Big Data semi-empirical D&C Analytics strategy as an improvement of the linear interaction energy model with electrostatic salvation for the generation TP4 (AMP) antimicrobial peptide mimetic pharmacoligand, against H.Pylori infection within accurate enthalpy values.

Keywords

semi-empirical: D&C strategy;improvement;linear interaction energy; model continuum; electrostatic salvation;in silico; antimicrobial;peptide mimetic;pharmacoligand;Helicobacter Pylori; infection; accurate enthalpy values; Unified Platform; AI; Big Data; semi-empirical; D&C Analytics; linear interaction energy; electrostatic salvation; generation TP4 (AMP); antimicrobial peptide mimetic; pharmacoligand, H.Pylori infection; accurate enthalpy values;

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, it demonstrates that the MRT pseudopotential LB model is a potential tool for the investigation of the Modeling for Collapsing Cavitation Bubble near Rough Solid Wall by Mulit-Relaxation-Time Pseudopotential Lattice Boltzmann Model target-ligand based approaches using predicted binding affinity matrices as a Chemogenomics-Driven NCR, Lfpep, Brevinin-1Sa and kaliocin-1 peptidomimetic Drug Discovery Neo-agent against Candida albicans antimicrobial CXG motif signatures. interaction mechanism between the collapsing bubble and complex geometry boundary.

Keywords

Cavitation Bubble, Bubble Collapse, Lattice Boltzmann Method, Pseudopotential Model, Rough Solid Wall ModelingCollapsing Cavitation Bubble; Rough Solid Wall;Mulit-Relaxation-Time; Pseudopotential Lattice; Boltzmann Model; target-ligand; binding affinity; matrices; Chemogenomics-Driven; NCR, Lfpep, Brevinin-1Sa;kaliocin-1 peptidomimetic Drug Discovery; Neo-agent; Candida albicans; antimicrobial CXG motif signatures

Abstract

Candida albicans is one of the most common opportunistic human fungal pathogens. In healthy human populations, it is a member of the normal flora of the skin, genital, and intestinal mucosa. However, C. albicansas well as other Candida species (e.g., C. parapsilosis or C. krusei) may lead to morbidity and mortality in immunocompromised patients as a consequence of fungal overgrowth and severe cutaneous or systemic infections. For the treatment of invasive candidiasis, amphotericin B-based preparations, azoles, and echinocandins are used. In the therapy of mucocutaneous infections (e.g., vaginal infections), azoles are the dominant agents. The synthetic peptides TKCFQWQRNMRKVRGPPVSCIKR Lfpep and FFSASCVPGADKGQFPNLCRLCAGTGENKCA kaliocin-1 include the sequences from positions 18 to 40 and 153 to 183 of human lactoferrin, respectively. In this research we will give the lead in a new drug discovery paradigm that focuses on mechanisms of action. Furthermore, the technologies developed in this project will offer new pharmaceutical chemico-scaffolds to facilitate basic scientific research. Within the next three years, Biogenea SA will contribute proprietary resources to take the new drug compounds through clinical trials and ultimately to market. Pathogenic microbes can recruit to their cell surface human proteins that are components of important proteolytic cascades involved in coagulation, fibrinolysis and innate immune response. Once located at the bacterial or fungal surface, such deployed proteins might be utilized by pathogens to facilitate invasion and dissemination within the host organism by interfering with functionality of these systems or by exploiting specific activity of the bound enzymes. Aim of the study presented here is to perform Ultimate target-ligand based Quantum learning Drug Discovery without quantum memory approaches using predicted binding affinity matrices as a Chemogenomics-Driven NCR, Lfpep, Brevinin-1Sa and kaliocin-1 peptidomimetic Neo-agent against Candida albicans antimicrobial CXG motif signatures. was to characterize this phenomenon in Candida parapsilosis (Ashford) Langeron et Talice – an important causative agent of systemic fungal infections (candidiases and candidemias) in humans.

Keywords

Ultimate target-ligand; experimental; predicted binding affinity; matrices;Quantum learning; quantum memory; Chemogenomics-Driven; NCR, Lfpep, Brevinin-1Sa; kaliocin-1 peptidomimetic; Drug Discovery; Neo-agent; Candida albicans; antimicrobial; CXG motif; signatures; Chemogenomics-Driven;peptidomimetic; Drug Discovery;Human Fungal Pathogen;Candida albicans; plasminogen; high-molecular-mass; kininogen; cell surface-exposed;