Abstract

It has been shown that the Oral administration for 6 days of 100 mg/kg MMK-1, of an agonist peptide selective for the FPRL1 receptor, suppressed alopecia induced by the anticancer drug etoposide in neonatal rats. However, the anti-alopecia effect of orally administered MMK-1 was inhibited by indomethacin, an inhibitor of cyclooxygenase (COX), or AH-23848B, an antagonist of the EP4 receptor for prostaglandin (PG) E2, suggesting involvement of PGE2 release and the EP4 receptor in the oral MMK-1 anti-alopecia mechanism. The anti-alopecia effect of orally administered MMK-1 was also blocked by an inhibitor of nuclear factor-kappaB (NF-kappaB), pyrrolidine dithiocarbamate, suggesting that the oral anti-alopecia effect of MMK-1 may be mediated by activation of NF-kappaB. These results suggested that MMK-1 bound to FPRL1 receptor might suppress etoposide-induced apoptosis of hair follicle cells and alopecia by way of PGE2 release and NF-kappaB activation. Previously, it has also been found that an intraperitoneally administered chemotactic peptide, N-formyl-Met-Leu-Phe (fMLP), and MMK-1, a selective agonist of formyl peptide receptor-like 1 (FPRL1) receptor, the low affinity subtype of the fMLP receptor, prevented the alopecia in neonatal rats induced by the anticancer agent etoposide. The anti-alopecia effect of fMLP was not inhibited at all by Boc-FLFLF, a selective antagonist of formylpeptide receptor (FPR), which is the high affinity subtype of the receptor, but it was partly inhibited by Trp-Arg-Trp-Trp-Trp-Trp-NH(2) (WRW(4)), an antagonist of FPRL1 receptor. The anti-alopecia effects of fMLP and MMK-1 were also inhibited by Lys-D-Pro-Thr (K(D)PT) and pyrrolidine dithiocarbamate, which are inhibitors of interleukin-1 (IL-1) and nuclear factor-kappaB (NF-kappaB) respectively. Computational methods utilizing the structural and functional information help to understand specific molecular recognition events between the target biomolecule and candidate hits and make it possible to design improved lead molecules for the target. The condition of a quantum Lyapunov-based control which can be well used in a closed quantum system is that the method can make the system convergent but not just stable. In the convergence study of the quantum Lyapunov control, two situations are classified: nondegenerate cases and degenerate cases. For these two situations, respectively, in this paper the target state is divided into four categories: the eigenstate, the mixed state which commutes with the internal Hamiltonian, the superposition state, and the mixed state which does not commute with the internal Hamiltonian. For these four categories, the quantum Lyapunov control methods for the closed quantum systems are summarized and analyzed. Particularly, the convergence of the control system to the different target states is reviewed, and how to make the convergence conditions be satisfied is summarized and analyzed. Here we represents a massive on-going scientific endeavor to provide a freely accessible state of the art software suite for protein and DNA targeted lead molecule of a Mechanistic in silico molecular recognized approach for the ligand based generation of a dual N-formyl-Met-Leu-Phe (fMLP), and MMK-1peptide mimetic agonists formyl-peptide hyper-agonist interactive receptors against chemotherapy-induced alopecia.

Keywords

mechanistic; in silico; molecular; recognized approach; ligand based; dual N-formyl-Met-Leu-Phe (fMLP), MMK-1peptide mimetic; hyper-agonist; fMLP targeted receptor; PGE2 EP4 pathway; chemotherapy-induced alopecia;

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 drug discovery approaches and proposes alternative approaches for increasing the of Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets A mechanistic in silico molecular recognized approach for the ligand based generation of a dual N-formyl-Met-Leu-Phe (fMLP), and MMK-1peptide mimetic hyper-agonist fMLP targeted receptor against the PGE2 EP4 pathway chemotherapy-induced alopecia.

Keywords

Complementary Approaches; Existing Target Based; Drug Discovery; Identifying Novel; Drug Targets; mechanistic; in silico; molecular recognized approach; ligand based; dual N-formyl-Met-Leu-Phe (fMLP), MMK-1peptide mimetic; hyper-agonist; fMLP targeted receptor; PGE2 EP4 pathway; chemotherapy-induced alopecia; drug discovery, drug design, drug targets, repositioning, molecular imaging;

Abstract

The condition of a quantum Lyapunov-based control which can be well used in a closed quantum system is that the method can make the system convergent but not just stable. In the convergence study of the quantum Lyapunov control, two situations are classified: nondegenerate cases and degenerate cases. For these two situations, respectively, in this paper the target state is divided into four categories: the eigenstate, the mixed state which commutes with the internal Hamiltonian, the superposition state, and the mixed state which does not commute with the internal Hamiltonian. For these four categories, the quantum Lyapunov control methods for the closed quantum systems are summarized and analyzed. Particularly, the convergence of the control system to the different target states is reviewed, and how to make the convergence conditions be satisfied is summarized and analyzed.Survey of Quantum Lyapunov Control MethodsA computer simulated 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.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 computationally simulated Survey of Quantum Lyapunov Control Methods of a gp100 Peptide pharmacophore mimic Vaccine-like and Interleukin-2 targeted as a 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.

Keywords

Survey of Quantum Lyapunov; Control Methods; computer simulated; gp100 Peptide; mimic designed pharmacophore; Vaccine-like; Interleukin-2; in silico; superagonist; clinical effect; Patients; Advanced Melanoma; Improved Algorithm; Chemically Tractable;, Semi-Automated Protein Inhibitor Design;

Abstract

Background

Adult human fibroblasts grown in low oxygen and with FGF2 supplementation have the capacity to tip the healing outcome of skeletal muscle injury—by favoring regeneration response in vivo over scar formation. Long-term reconstituting (LTR) hematopoietic stem cells (LT-HSCs) are the source of all circulating blood cells and are defined by their capacity for self-renewal and multilineage differentiation.TLR4, the receptor for LPS, and TLR3 also signal through the adaptor protein, Toll/IL-1 resistance domain-containing adaptor-inducing IFN-β (TRIF)/Toll-IL1 receptor domain-containing adaptor molecule 1, which leads to IFN-I production. Molecular Mechanics (MM) is the method of choice for computational studies of biomolecular systems owing to its modest computational cost, which makes it possible to routinely perform molecular dynamics (MD) simulations on chemical systems of biophysical and biomedical relevance.

Scope of Review

As one of the main factors limiting the accuracy of MD results is the empirical force field used, the present paper offers a review of recent developments in the CHARMM additive force field, one of the most popular bimolecular force fields. Additionally, we present a detailed discussion of the CHARMM Drude polarizable force field, anticipating a growth in the importance and utilization of polarizable force fields in the near future. Throughout the discussion emphasis is placed on the force fields’ parametrization philosophy and methodology.

Major Conclusions

Recent improvements in the CHARMM additive force field are mostly related to newly found weaknesses in the previous generation of additive force fields. Beyond the additive approximation is the newly available CHARMM Drude polarizable force field, which allows for MD simulations of a CHARMM additive and polarizable force fields for biophysics and computer-aided Cutting Edge Combinatorial Designed Agonistic Stem Cell Inducer Based on a FGF2-Overexpressed Transcriptomic Effect with a Regeneration Competence in Cord Blood Tetanus Toxoid TLR-Free Signaling Associated Simulated Mechanistic Stem Cell Culture System.

General Significance

Addressing the limitations ensures the reliability of the new CHARMM36 additive force field for the types of calculations that are presently coming into routine computational reach while the availability of the Drude polarizable force fields offers a model that is an inherently more accurate model of the underlying physical forces driving a novel a CHARMM additive and polarizable force fields for biophysics and computer-aided Cutting Edge Combinatorial Designed Agonistic Stem Cell Inducer Based on a FGF2-Overexpressed Transcriptomic Effect with a Regeneration Competence in Cord Blood Tetanus Toxoid TLR-Free Signaling Associated Simulated Mechanistic Stem Cell Culture System.

Keywords

CHARMM additive; polarizable force fields; biophysics; computer-aided; Cutting Edge; Combinatorial Designed; Agonistic Stem Cell Inducer; FGF2-Overexpressed; Transcriptomic Effect; Regeneration Competence; Cord Blood; Tetanus Toxoid; TLR-Free; Signaling Associated; Simulated; Mechanistic; Stem Cell; Culture System; molecular dynamics, empirical force field, potential energy function, molecular mechanics, computer-aided drug design, biophysics;

Abstract

Proton transport plays an important role in biological energy transduction and sensory systems. Therefore it has attracted much attention in biological science and biomedical engineering in the past few decades. The present work proposes a multiscale/multiphysics model for the understanding of the molecular mechanism of proton transport in transmembrane proteins involving continuum, atomic and quantum descriptions, assisted with the evolution, formation and visualization of membrane channel surfaces. We describe proton dynamics quantum mechanically via a new density functional theory based on the Boltzmann statistics, while implicitly model numerous solvent molecules as a dielectric continuum to reduce the number of degrees of freedom. The density of all other ions in the solvent is assumed to obey the Boltzmann distribution in a dynamic manner. The impact of protein molecular structure and its charge polarization on the proton transport is considered explicitly at the atomic scale. A variational solute-solvent interface is designed to separate the explicit molecule and implicit solvent regions. The variational principle is employed to derive coupled governing equations for the proton transport system. Generalized Laplace-Beltrami equation, generalized Poisson-Boltzmann equation and generalized Kohn-Sham equation are obtained from the present variational framework. The variational solvent-solute interface is generated and visualized to facilitate the multiscale discrete/continuum/quantum descriptions. Theoretical formulations for the proton density and conductance are constructed based on fundamental laws of physics. A number of mathematical algorithms, including the Dirichlet to Neumann mapping (DNM), matched interface and boundary (MIB) method, Gummel iteration, and Krylov space techniques are utilized to implement the proposed model in a computationally efficient manner. The Gramicidin A (GA) channel is used to validate the performance of the proposed proton transport model and demonstrate the efficiency of the proposed mathematical algorithms. The proton channel conductances are studied over a number of applied voltages and reference concentrations. A comparison with experimental data verifies the present model predictions and confirms the proposed model. We formulate a total free energy functional to put proton kinetic and potential energies, the free energy of all other ions, the polar and nonpolar energies of the whole system on an equal footing Quantum dynamics in continuum for Variational solvent-solute interface proton transport II as a semi-empirical D&C linear interaction energy electrostatic salvation strategy for the generation TP4 (AMP) antimicrobial peptide mimetic pharmacoligand, against H.Pylori infection within accurate enthalpy values.

Keywords

semi-empirical; D&C strategy; linear interaction energy; continuum electrostatic salvationin; in silico generation; antimicrobial peptide; mimetic pharmacoligand; Helicobacter Pylori; enthalpy values; Quantum dynamics; continuum for proton transport II; Variational solvent-solute interface; TP4 (AMP); enthalpy values. Proton transport, Quantum dynamics; Multiscale model, Laplace-Beltrami equation, Poisson-Boltzmann equation, Kohn-Sham equation, Variational principle;

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 drug discovery approaches and proposes alternative approaches for increasing the of Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets A mechanistic in silico molecular recognized approach for the ligand based generation of a dual N-formyl-Met-Leu-Phe (fMLP), and MMK-1peptide mimetic hyper-agonist fMLP targeted receptor against the PGE2 EP4 pathway chemotherapy-induced alopecia.

Keywords

Complementary Approaches; Existing Target Based; Drug Discovery; Identifying Novel; Drug Targets; mechanistic; in silico; molecular recognized approach; ligand based; dual N-formyl-Met-Leu-Phe (fMLP), MMK-1peptide mimetic; hyper-agonist; fMLP targeted receptor; PGE2 EP4 pathway; chemotherapy-induced alopecia; drug discovery, drug design, drug targets, repositioning, molecular imaging;

Abstract

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 structure-based drug design in resulting for the first time the efficient generation of logical computations using algorithmic self-assembly of RGD-FHRRIKA-RARADADA-IKVAV responsive peptide-modified mimetic triple-crossover for the figuring of novel hydrothermochemic molecules for tissue regeneration.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 structure-based CHARMM additive and polarizable force fields for biophysics and computer-aided drug design Logical computations using algorithmic self-assembly of 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;Logical computations;algorithmic self-assembly;peptide-modified; mimetic;triple-crossover; CHARMM additive; polarizable force fields; biophysics; computer-aided drug design;

Abstract

Proton transport plays an important role in biological energy transduction and sensory systems. Therefore it has attracted much attention in biological science and biomedical engineering in the past few decades. The present work proposes a multiscale/multiphysics model for the understanding of the molecular mechanism of proton transport in transmembrane proteins involving continuum, atomic and quantum descriptions, assisted with the evolution, formation and visualization of membrane channel surfaces. We describe proton dynamics quantum mechanically via a new density functional theory based on the Boltzmann statistics, while implicitly model numerous solvent molecules as a dielectric continuum to reduce the number of degrees of freedom. The density of all other ions in the solvent is assumed to obey the Boltzmann distribution in a dynamic manner. The impact of protein molecular structure and its charge polarization on the proton transport is considered explicitly at the atomic scale. A variational solute-solvent interface is designed to separate the explicit molecule and implicit solvent regions. We formulate a total free energy functional to put proton kinetic and potential energies, the free energy of all other ions, the polar and nonpolar energies of the whole system on an equal footing. The variational principle is employed to derive coupled governing equations for the proton transport system. Generalized Laplace-Beltrami equation, generalized Poisson-Boltzmann equation and generalized Kohn-Sham equation are obtained from the present variational framework. The variational solvent-solute interface is generated and visualized to facilitate the multiscale discrete/continuum/quantum descriptions. Theoretical formulations for the proton density and conductance are constructed based on fundamental laws of physics. A number of mathematical algorithms, including the Dirichlet to Neumann mapping (DNM), matched interface and boundary (MIB) method, Gummel iteration, and Krylov space techniques are utilized to implement the proposed model in a computationally efficient manner. The Gramicidin A (GA) channel is used to validate the performance of the proposed proton transport model and demonstrate the efficiency of the proposed mathematical algorithms. The proton channel conductances are studied over a number of applied voltages and reference concentrations. A comparison with experimental data verifies the present model predictions and confirms the proposed Quantum model dynamics in continuum for proton transport II Variational solvent-solute interface computer-aided drug design Logical computations using algorithmic self-assembly of RGD-FHRRIKA-RARADADA-IKVAV responsive peptide-modified mimetic triple-crossover hydrothermochemic molecules for tissue regeneration.

Keywords

Quantum dynamics; continuum for proton transport II; Variational solvent-solute interface; computer-aided drug design;Logical computations; algorithmic self-assembly; RGD-FHRRIKA-RARADADA-IKVAV; responsive peptide-modified; mimetic; triple-crossover; hydrothermochemic molecules; tissue regeneration;Proton transport, Quantum dynamics in continuum, Multiscale model, Laplace-Beltrami equation, Poisson-Boltzmann equation, Kohn-Sham equation, Variational principle;

Abstract

The Schrödinger perturbation energy for an arbitrary order N of the perturbation has been presented with the aid of a circular scale of time. The method is of a recurrent character and developed for a non-degenerate quantum state. It allows one to reduce the inflation of terms necessary to calculate known from the Feynman’s diagrammatical approach to a number below that applied in the original Schrödinger perturbation theory in QM/MM rational drug discovery and molecular diversity for the construction of a Circular Scale of Time as a Way of Calculating the Quantum-Mechanical Perturbation Energy Given by the Schrödinger Method towards an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote.

Keywords

Quantum-Mechanical Perturbation Energy, Circular Scale of Time;QM/MM; rational drug discovery; molecular diversity; construction of a Circular Scale Time; Way of Calculating; Quantum-Mechanical; Perturbation Energy; Given Schrödinger Method; anti-alpha-bungarotoxin; MAP-p6.7; peptide; mimetic; ligand; nicotinic receptor; binding site; snake neurotoxin; synthetic antidote;

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. We experimentally demonstrate the feasibility of this approach with an example 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.A variational eigenvalue solver on a photonic quantum processorThe role of QM/MM in rational drug discovery and molecular diversity for the construction of an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote. In chemistry, the properties of atoms and molecules can be determined by solving the Schrödinger equation. However, because the dimension of the problem grows exponentially with the size of the physical system under consideration, exact treatment of these problems remains classically infeasible for compounds with more than 2–3 atoms1. Many approximate methods2 have been developed to treat these systems, but efficient, exact methods for large chemical problems remain out of reach for classical computers. Beyond chemistry, the solution of large eigenvalue problems3 would have applications ranging from determining the results of internet search engines4 to designing new materials and drugs5.A variational eigenvalue solver on a photonic quantum processorThe role of QM/MM in rational drug discovery and molecular diversity for the construction of an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote. Recent developments in the field of quantum computation offer a way forward for determining efficient solutions of many instances of large eigenvalue problems that are classically intractable6,7,8,9,10,11,12. Quantum approaches to finding eigenvalues have previously relied on the quantum phase estimation (QPE) algorithm. The QPE algorithm offers an exponential speedup over classical methods and requires a number of quantum operations O(p−1) to obtain an estimate with precision p (refs 13, 14, 15, 16, 17, 18). In the standard formulation of QPE, one assumes the eigenvector |ψ› of a Hermitian operator is given as input and the problem is to determine the corresponding eigenvalue λ. The time the quantum computer must remain coherent is determined by the necessity of O(p−1) successive applications of , each of which can require on the order of millions or billions of quantum gates for practical applications17,19, as compared to the tens to hundreds of gates achievable in the short term. Here, we introduce an alternative to QPE that significantly reduces the requirements for coherent evolution. We have developed a reconfigurable quantum processing unit (QPU), which efficiently calculates the expectation value of a Hamiltonian (), providing an exponential speedup over exact diagonalization, the only known exact solution to the problem on a traditional computer. 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. The QPU has been experimentally implemented using integrated photonics technology with a spontaneous parametric downconversion single-photon source and combined with an optimization algorithm run on a classical processing unit (CPU), which variationally computes the eigenvalues and eigenvectors of a variational algorithm, this approach reduces the requirement for coherent evolution of the quantum state, making more efficient use of quantum resources, and may offer an alternative route to practical quantum-enhanced computation of QM/MM variational eigenvalue solvers on a photonic quantum processor in rational drug discovery and molecular diversity for the construction of an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote.

Keywords

Evaluation, Inverse Molecular Design Algorithm, Model Binding Site, In silico predicted, computer-aided molecular designed CTLA-4 blockador, increasement, antigen-specific CD8+ T-cells, inprevaccinated patients, melanoma, new cluster, algorithms, Large-Scale Protein-Ligand Docking experiment, inverse design, scoring function, protein-ligand interaction, cytochrome c peroxidase, dead-end elimination, drug design