Abstract

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 distribution of quantum Fisher information in asymmetric cloning 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.

Keywords

computational; target fishing; mining machinery;novel target; Identification tool;predicting therapeutic; proinsulin; GLP-1; INGAP-Ppeptide; mimetic;insulinotropic; compounds;chemogenomic;database,Distribution; quantum; Fisher; information; asymmetric; cloning machines;

Abstract

An unknown quantum state cannot be copied and broadcast freely due to the no-cloning theorem. Approximate cloning schemes have been proposed to achieve the optimal cloning characterized by the maximal fidelity between the original and its copies. Here, from the perspective of quantum Fisher information (QFI), we investigate the distribution of QFI in asymmetric cloning machines which produce two nonidentical copies. As one might expect, improving the QFI of one copy results in decreasing the QFI of the other copy. It is perhaps also unsurprising that asymmetric phase-covariant cloning outperforms universal cloning in distributing QFI since a priori information of the input state has been utilized. However, interesting results appear when we compare the distributabilities of fidelity (which quantifies the full information of quantum states), and QFI (which only captures the information of relevant parameters) in asymmetric cloning machines. Unlike the results of fidelity, where the distributability of symmetric cloning is always optimal for any d-dimensional cloning, we find that any asymmetric cloning outperforms symmetric cloning on the distribution of QFI for d ≤ 18, whereas some but not all asymmetric cloning strategies could be worse than symmetric ones when d > 18. Classical information can be replicated perfectly and broadcast without fundamental limitations. However, information encoded in quantum states is subject to several intrinsic restrictions of quantum mechanics, such as Heisenberg’s uncertainty relations1 and quantum no-cloning theorem2. The no-cloning theorem tells us that an unknown quantum state cannot be perfectly replicated because of the linearity of the time evolution in quantum physics, which is the essential prerequisite for the absolute security of quantum cryptography3. Nevertheless, it is still possible to clone a quantum state approximately, or instead, clone it perfectly with certain probability4,5 distributions of a computational target fishing mining quantum Fisher information in asymmetric cloning 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.

Keywords

Distribution of quantum; Fisher information; asymmetric cloning machines; computational target fishing; mining machinery; Identification tool; predicting therapeutic; GLP-1, INGAP-P; IGLHDPSHGTLPNGS peptide; mimetic insulinotropic; high-potency; chemogenomic databases;

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 of 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 and IGLHDPSHGTLPNGS peptide mimetic insulinotropic of high-potency compounds based on chemogenomic databases.

Keywords

Circular Scale of Time, Schrödinger Perturbation Theory, Non-Degenerate Quantum State; Circular Scale of Time; Energy of a Quantum State; Schrödinger Perturbation Theory; identification tool; GLP-1, INGAP-P;IGLHDPSHGTLPNGS peptide mimetic; insulinotropic; high-potency; compounds; chemogenomic databases;

Abstract

An unknown quantum state cannot be copied and broadcast freely due to the no-cloning theorem. Approximate cloning schemes have been proposed to achieve the optimal cloning characterized by the maximal fidelity between the original and its copies. Here, from the perspective of quantum Fisher information (QFI), we investigate the distribution of QFI in asymmetric cloning machines which produce two nonidentical copies. As one might expect, improving the QFI of one copy results in decreasing the QFI of the other copy. It is perhaps also unsurprising that asymmetric phase-covariant cloning outperforms universal cloning in distributing QFI since a priori information of the input state has been utilized. However, interesting results appear when we compare the distributabilities of fidelity (which quantifies the full information of quantum states), and QFI (which only captures the information of relevant parameters) in asymmetric cloning machines. Unlike the results of fidelity, where the distributability of symmetric cloning is always optimal for any d-dimensional cloning, we find that any asymmetric cloning outperforms symmetric cloning on the distribution of QFI for d ≤ 18, whereas some but not all asymmetric cloning strategies could be worse than symmetric ones when d > 18. Classical information can be replicated perfectly and broadcast without fundamental limitations. However, information encoded in quantum states is subject to several intrinsic restrictions of quantum mechanics, such as Heisenberg’s uncertainty relations1 and quantum no-cloning theorem2. The no-cloning theorem tells us that an unknown quantum state cannot be perfectly replicated because of the linearity of the time evolution in quantum physics, which is the essential prerequisite for the absolute security of quantum cryptography3. Nevertheless, it is still possible to clone a quantum state approximately, or instead, clone it perfectly with certain probability4,5 distributions of a computational target fishing mining quantum Fisher information in asymmetric cloning 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.

Keywords

Distribution of quantum; Fisher information; asymmetric cloning machines; computational target fishing; mining machinery; Identification tool; predicting therapeutic; GLP-1, INGAP-P; IGLHDPSHGTLPNGS peptide; mimetic insulinotropic; high-potency; chemogenomic databases

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 of 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 and IGLHDPSHGTLPNGS peptide mimetic insulinotropic of high-potency compounds based on chemogenomic databases.

Keywords

Circular Scale of Time, Schrödinger Perturbation Theory, Non-Degenerate Quantum State; Circular Scale of Time; Energy of a Quantum State; Schrödinger Perturbation Theory; identification tool; GLP-1, INGAP-P;IGLHDPSHGTLPNGS peptide mimetic; insulinotropic; high-potency; compounds; chemogenomic databases;

Abstract

Near linear scaling fragment based quantum chemical calculations are becoming increasingly popular for treating large systems with high accuracy and is an active field of research. However, it remains difficult to set up these calculations without expert knowledge. To facilitate the use of such methods, software tools need to be available to support these methods and help to set up reasonable input files which will lower the barrier of entry for usage by non-experts. Previous tools relies on specific annotations in structure files for automatic and successful fragmentation such as residues in PDB files. We present a general fragmentation methodology and accompanying tools called FragIt to help setup these calculations. FragIt uses the SMARTS language to locate chemically appropriate fragments in large structures and is applicable to fragmentation of any molecular system given suitable SMARTS patterns. We present SMARTS patterns of fragmentation for proteins, DNA and polysaccharides, specifically for D-galactopyranose for use in cyclodextrins. FragIt is used to prepare input files for the Fragment Molecular Orbital method in the GAMESS program package, but can be extended to other computational methods easily.FragIt: A Tool to Prepare Input Files for Fragment Based Quantum Chemical CalculationsA rational in silico drug-target flexibility complement methodology-design for the generation of a peptide-mimic novel pharmacoelement binding to the amino acid conserved sequences of the active loop of a Haemophilus influenzae porin P2.FragIt: A Tool to Prepare Input Files for Fragment Based Quantum Chemical Calculations. Haemophilus influenzae type b (Hib) is one of the leading causes of invasive bacterial infection in young children. It is characterized by inflammation that is mainly mediated by cytokines and chemokines. One of the most abundant components of the Hib outer membrane is the P2 porin, which has been shown to induce the release of several inflammatory cytokines. A synthetic peptide corresponding to loop L7 of the porin activates JNK and p38 mitogen-activated protein kinase (MAPK) pathways. It has also been reported that a novel use of the complementary peptide approach to design a peptide that is able to bind selectively to the protein P2, thereby reducing its activity. In this in silico study we used of higher levels of our complement conserved structure ligand based binding pocket drug interactive theory to increase the accuracy of protein-ligand binding affinity predictions, resulting in better hit identification success rates as well as more efficient lead optimization processes. Here, we discovered for the first time the GENEA-Poriflunzaten-5567 a Peptide-mimic novel pharmacoelements complementary to the active loop of porin P2 from Haemophilus influenzae for the annotated modulation of its activity using a rational in silico drug-target flexibility complement methodology-design to Prepare Input Files for Fragment Based Quantum Chemical Calculations for the generation of a peptide-mimic novel pharmacoelement binding to the amino acid conserved sequences of the active loop Haemophilus influenzae porin P2.

Keywords

combined-application;knowledge-based;scoring;physical;-forcefield;-based hit-scoring;functions; rational; in silico drug-target; flexibility; complement; methodology-design; peptide-mimic; novel pharmacoelement; amino acid; conserved sequences; active loop; Haemophilus influenzae; porin P2;. Input Files; Fragment Based; Quantum Chemical Calculations;

Abstract

The need to compute molecular properties for larger and larger systems with desirable accuracy has led to the development of novel methods such as fragmentation methods [1]. In fragmentation methods, a large system is divided into several smaller subsystems called fragments. Each fragment is treated with some ab initio level of theory and different methods [2]–[7] include the surrounding environment in different ways. In this work, we are interested in setting up Fragment Molecular Orbital (FMO) [5], [6] and Effective Fragment Molecular Orbital (EFMO) [8], [9] calculations, but our method is extensible to other fragment based methods. In the FMO method, each fragment is polarized by the presence of the Coulomb field of all other fragments. The underlying equations allow for a systematic improvement of the energy by considering pairs and optionally triples of fragments [10], the latter often within milihartree accuracy of the corresponding ab initio energy. FMO supports correlated treatment of one or more fragments [11]–[13] as well the possibility of obtain excitation energies with good accuracy. [14] The FMO method in GAMESS [15] utilizes a novel parallelization scheme [16] to allow computations to be carried out efficiently on desktop computers as well as large scale super computers. [17] Fragmentation can occur across covalent bonds using either the Hybrid Orbital Projection (HOP) [18] or Adapted Frozen Orbital (AFO) [19], [20] method. The EFMO method, also available in GAMESS, neglects the Coulomb bath from FMO and replaces it with classical terms to improve the computational speed to Prepare Input Files for Fragment Based Quantum Chemical Calculations for in silico drug-target flexibility complement methodology-design for the generation of a peptide-mimic novel pharmacoelement binding to the active loop of a Haemophilus influenzae porin P2 amino acid conserved sequences.

Keywords

rational Tool; Input Files; Fragment Based; Quantum Chemical Calculations; drug-target flexibility; complement methodology-design; peptide-mimic; novel pharmacoelement; active loop; Haemophilus influenzae; porin P2; amino acid; conserved sequences.

Abstract

Near linear scaling fragment based quantum chemical calculations are becoming increasingly popular for treating large systems with high accuracy and is an active field of research. However, it remains difficult to set up these calculations without expert knowledge. To facilitate the use of such methods, software tools need to be available to support these methods and help to set up reasonable input files which will lower the barrier of entry for usage by non-experts. Previous tools relies on specific annotations in structure files for automatic and successful fragmentation such as residues in PDB files. We present a general fragmentation methodology A Tool to Rationally in silico Identification of a immunogenic MAGED4B peptide-mimetic Prepare Input Files for Fragment Based Quantum Chemical Calculations on pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies and accompanying tools to help setup these calculations.

Keywords

genetic-algorithm;(meta)-ensembles-approach;binary-classification;ligand-based;drug, design; MAGED4; Boral cancer immunotherapies, Input Files; Fragment Based Quantum; Chemical Calculations; Rationally; in silico Identification; immunogenic; MAGED4B peptide-mimetic; pharmacophoric; robust agent; fragment-library; drug-compound; vaccine mimic annotated properties; oral cancer immunotherapies.

Abstract

Background

Molecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands. Typically, molecular docking algorithms consider receptors as rigid bodies. Receptors are, however, intrinsically flexible in the cellular environment. The use of a time series of receptor conformations is an approach to explore its flexibility in molecular docking computer simulations, but it is extensively time-consuming. Hence, selection of the most promising conformations can accelerate docking experiments and, consequently, the RDD efforts. It has been previosuly reported that lipopeptides can be used to elicit cytotoxic T lymphocyte (CTL) responses against viral diseases and cancer. In previous scientific projects, it has also been determined that mono-palmitoylated peptides can enhance anti-tumor responses in the absence of adjuvant activity. To investigate whether di-palmitoylated peptides with TLR2 agonist activity are able to induce anti-tumor immunity, it was previously synthesized a di-palmitic acid-conjugated long peptide that contains a murine CTL epitope of HPV E749-57 (Pam2IDG). Pam2IDG stimulated the maturation of bone marrow-derived dendritic cells (BMDCs) through TLR2/6. After immunization, Pam2IDG induced higher levels of T cell responses than those obtained with its non-lipidated counterpart (IDG). Here, we present a novel approach based on GRID molecular interaction fields and the derivative peptide mimicking rationally drug discovery method that has been previously utilized, which may provides a common reference to compare both small molecule ligands and conserved fragment-peptide targeting. Unlike classical pharmacophore elucidation approaches that extract simplistic molecular features, determine those which are common across the data set, and use these features to align the structures and subsequently extracts the common interacting features in terms of their molecular interaction fields, pseudofields, and atomic points, representing the common pharmacophore as a more comprehensive pharmacophoric pseudomolecule. Our fragment-ligand based drug discovery approach is applied to a number of data sets to investigate performance in terms of reproducing the X-ray crystallography-based alignment, in terms of its discriminatory ability when applied to virtual screening and also to illustrate its ability to explain alternative binding modes. As a result we discovered for the first time the GENEA-Tollarepomir-5579, a Toll-like receptor agonist-conjugated peptide-mimetic pharmacophoric multi-targeted agent utilizing a comprehensive source and free tool for assessment of an in silico annotated drug discovery interactive approach for Mining flexible-receptor docking experiments to select promising protein receptor snapshots on the depletion of tumor-associated macrophages by a computer-aided designed canditate druggable Toll-like receptor (Pam2IDG) peptide-domain targeted by a pharmacophoric mimetic agonistic agent.

Keywords

Toll-likereceptor;agonist-conjugated;peptide-mimetic;pharmacophoric;multi-targeted, Mining flexible-receptor docking experiments; select promising protein receptor; snapshots; in silico; annotated drug discovery interactive approach; depletion tumor-associated macrophages; computer-aided; canditate druggable; Toll-like receptor; (Pam2IDG) peptide-domain; targeted; pharmacophoric; mimetic agonistic agent.

Abstract

Steering quantum dynamics such that the target states solve classically hard problems is paramount to quantum simulation and computation. And beyond, quantum control is also essential to pave the way to quantum technologies. Here, important control techniques are reviewed and presented in a unified frame covering quantum computational gate synthesis and spectroscopic state transfer alike. We emphasize that it does not matter whether the quantum states of interest are pure or not. While pure states underly the design of quantum circuits, ensemble mixtures of quantum states can be exploited in a more recent class of algorithms: it is illustrated by characterizing the Jones polynomial in order to distinguish between different (classes of) knots. Further applications include Josephson elements, cavity grids, ion traps and nitrogen vacancy centres in scenarios of closed as well as open quantum systems.Control aspects of quantum computing using pure and mixed states Control aspects of quantum computing using pure and mixed states A Computer-aided rational approach for the in silico generation of a TCR Peptide Mimetic Pharmacoligand as a potential chemo-modulator in Human Autoimmune Diseases.Inflammatory Th1 cells reacting to tissue/myelin derived antigens likely contribute to the pathogenesis of diseases such as multiple sclerosis (MS), rheumatoid arthritis (RA), and psoriasis. One regulatory mechanism that may be useful for treating autoimmune diseases involves an innate second set of Th2 cells specific for portions of the T cell receptor of clonally expanded pathogenic Th1 cells. These Th2 cells are programmed to respond to internally modified V region peptides from the T cell receptor (TCR) that are expressed on the Th1 cell surface in association with major histocompatibility molecules. TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on quantum computing pure and mixed states of a Computer-aided rational approach for the in silico generation of a TCR Peptide Mimetic Pharmacoligand as a potential chemo-modulator in Human Autoimmune Diseases.

Keywords

Control aspects of quantum computing; pure and mixed states; Computer-aided rational approach; in silico; TCR Peptide; Mimetic Pharmacoligand; chemo-modulator; Human Autoimmune Diseases, optimal quantum control, quantum computing, unitary gate design, knot theory, Jones polynomial.