Abstract

ASARM-peptides are substrates and ligands for PHEX, the gene responsible for X-linked hypophosphatemic rickets (HYP). PHEX binds to the DMP1-ASARM-motif to form a trimeric-complex with α5β3-integrin on the osteocyte surface and this suppresses FGF23 expression. ASARM-peptide disruption of this complex increases FGF23 expression. A 4.2kDa peptide (SPR4) has been previously used that binds to ASARM-peptide and ASARM-motif to DMP1-PHEX interact and by assessing SPR4 for treating inherited hypophosphatemic rickets. Here, we discovered for the first time the GENEA-Bonespemitron-5527, a Computer-aided designed of a SPR4-peptide-mimetic pharmacophoric super-agonist for the regulation of bone metabolism utilizing Computational modelling of biomolecular simulation methods in structural biology interfaces between physics, chemistry and biology on an atomistic scalable literature computer-based discovery of an annotated SPR4-peptide-similar multi-molecular pharmacophoric reverse docked super-agonist scaffold as a canditate bone metabolism regulator.

Keywords

SPR4 peptide mimetic; pharmacophoric; super agonist; regulation; bone-metabolism; scalable Literature Based; β-catenin; Computational modelling; biomolecular systems; simulation methods; structural biology; interfaces; physics chemistry and biology in atomistic biomolecular simulation scalable literature;

Abstract

Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug–drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). In this study we analyse the impact of bone active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using assessments of comparison of dynamic and static algorithmic models for predicting drug–drug interactions via inhibition mechanisms for a scalable literature Computer-based discovery of an annotated SPR4-peptide-similar multi-molecular reverse docked super-agonist pharmacophoric scaffold as a canditate bone metabolism regulator.

Keywords

Assessment algorithms; predicting drug–drug interactions; via inhibition mechanisms; comparison; dynamic; static models; scalable literature; Computer-based discovery; annotated SPR4-peptide-similar; multi-molecular pharmacophoric; reverse docked; super-agonist scaffold; canditate regulator; bone metabolism;

Abstract

One of the largest challenges of computational chemistry is calculation of accurate free energies for the binding of a small molecule to a biological macromolecule, which has immense implications in drug development. It is well-known that standard molecular-mechanics force fields used in most such calculations have a limited accuracy. Therefore, there has been a great interest in improving the estimates using quantum-mechanical (QM) methods. We review here approaches involving explicit QM energies to calculate binding affinities, with an emphasis on the methods, rather than on specific applications. Many different QM methods have been employed, ranging from semiempirical QM calculations, via density-functional theory, to strict coupled-cluster calculations. Dispersion and other empirical corrections are mandatory for the approximate methods, as well as large basis sets for the stricter methods. QM has been used for the ligand, for a few crucial groups around the ligand, for all the closest atoms (200–1000 atoms), or for the full receptor–ligand complex, but it is likely that with a proper embedding it might be enough to include all groups within ∼6 Å of the ligand. Approaches involving minimized structures, simulations of the end states of the binding reaction, or full free-energy simulations have been tested in this study on an atomistic scalable literature computer-based discovery of an annotated SPR4-peptide-similar multi-molecular pharmacophoric reverse docked super-agonist scaffold as a canditate bone metabolism regulator.

Abstract

Molecular simulation is increasingly demonstrating its practical value in the investigation of biological systems. Computational modelling of biomolecular systems is an exciting and rapidly developing area, which is expanding significantly in scope. A range of simulation methods has been developed that can be applied to study a wide variety of problems in structural biology and at the interfaces between physics, chemistry and biology. Here, we give an overview of methods and some recent developments in atomistic biomolecular simulation. Some recent applications and theoretical developments are highlighted. In the work, we propose an approach to “smart design” of heterostructures (quantum wells and superlattices) based on the combination of Inverse Scattering Problem Method and the direct solution of the eigenvalue problem for the Schrödinger equation with reconstructed potentials. Potential shape reconstructed in this way can be substituted then by some approximation, so that the output spectrum obtained by solving the Schrödinger equation with such approximated potential, differs only slightly from the input one. In our opinion, the approach can be used in many applications, for instance, for developing the new electronic devices such as tunable THz detectors. 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 Molecular simulation methods in arational in silico drug-target flexibility complement “Smart Design” methodology of Quantum Wells and Double-Quantum Wells Structures 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.

Keywords

combined-applicationknowledge-basedpose-scoringphysical-forcefield-basedhit-scoringfunctions “Smart Design” of Quantum Wells and Double-Quantum Wells Structures A 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, biomolecular simulation, molecular modelling, molecular dynamics, force fields, quantum mechanics/molecular mechanics, quantum chemical modelling

Abstract

In this paper, a novel neural network is proposed based on quantum rotation gate and controlled- NOT gate. Both the input layer and the hide layer are quantum-inspired neurons. The input is given by qubits, and the output is the probability of qubit in the state. By employing the gradient descent method, a training algorithm is introduced. The experimental results show that this model is superior to the common BP networks in Quantum-Inspired Neural Networks with Application 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.

Keywords

Quantum-Inspired,Neural Networks;Application;rational in silico;drug-target;flexibility;complement methodology-design;generation peptide-mimic;novel pharmacoelement;binding amino acid;conserved sequences; active loop; Haemophilus influenzae porin P2.

Abstract

Molecular simulation is increasingly demonstrating its practical value in the investigation of biological systems. Computational modelling of biomolecular systems is an exciting and rapidly developing area, which is expanding significantly in scope. A range of simulation methods has been developed that can be applied to study a wide variety of problems in structural biology and at the interfaces between physics, chemistry and biology. Here, we give an overview of methods and some recent developments in atomistic biomolecular simulation. Some recent applications and theoretical developments are highlighted. The ever-increasing number of tumor-associated antigens has provided a major stimulus for the development of therapeutic peptides vaccines. Tumor-associated peptides can induce high immune response rates and have been developed as vaccines for several types of solid tumors, and many are at various stages of clinical testing. MAGED4B, a melanoma antigen, is overexpressed in oral squamous cell carcinoma (OSCC) and this expression promotes proliferation and cell migration. In previous scientifc projects it has also been identified that 9 short peptides derived from MAGED4B protein are restricted in binding to the HLA subtypes common in the Asian population (HLA-A2, A11, and A24). As a result, we here discovered for the first time the GENEA-Immunomagetor-45700d utilizing the In silico rational Biomolecular simulation and modelling: status, progress and prospects identifications of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies.

Keywords

genetic-algorithm;(meta)-ensembles-approachbinary-classification;ligand-baseddrug-design;MAGED4B;oral cancer immunotherapies; Biomolecular simulation; modelling; status; progress; prospects;Rationally; in silico Identification; immunogenic MAGED4B; peptide-mimetic pharmacophoric; robust agent; potential fragment-library; drug-compound; comprising vaccine mimic; annotated properties; oral cancer immunotherapies;

Abstract

Strategies for CADD vary depending on the extent of structural and other information available regarding the target (enzyme/receptor) and the ligands. Computer-aided drug design (CADD) is an exciting and diverse discipline where various aspects of applied and basic research merge and stimulate each other. In the early stage of a drug discovery process, researchers may be faced with little or no structure activity relationship (SAR) information. The process by which a new drug is brought to market stage is referred to by a number of names most commonly as the development chain or “pipeline” and consists of a number of distinct stages. To design a rational drug, we must firstly find out which proteins can be the drug targets in pathogenesis. In present review we reported a CADD, DNA as target, receptor theory, structure optimization, structure-based drug design, virtual high-throughput screening (vHTS), Computer-Aided Drug Design graph machines as Innovative Tools for the in silico Modeling Identification of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies. Rationally in silico Identification of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies.

Keywords

CADD; HTS; Software;General Purpose; Molecular Modeling; SBDD, Computer-Aided Drug Design; Innovative Tool; Modeling Rationally; in silico Identification; immunogenic; MAGED4B; peptide-mimetic; pharmacophoric; robust agent; potential; fragment-library; drug-compound; vaccine mimic; annotated properties; oral cancer immunotherapies;

Abstract

As high-throughput biochemical screens are both expensive and labor intensive, researchers in academia and industry are turning increasingly to virtual-screening methodologies. Virtual screening relies on scoring functions to quickly assess ligand potency. Although useful for in silico ligand identification, these scoring functions generally give many false positives and negatives; indeed, a properly trained human being can often assess ligand potency by visual inspection with greater accuracy. Given the success of the human mind at protein−ligand complex characterization, we present here a scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain. Computer-aided drug design depends on fast and accurate scoring functions to aid in the identification of small-molecule ligands. The NNScore: Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes Computer-Aided Drug Design scoring function presented here as an Innovative Tool for the in silico Identification of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies, used either in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.

Keywords

NNScore; Neural-Network-Based; Scoring Function; Characterization; Protein−Ligand Complexes; Computer-Aided Drug Design: immunogenic; MAGED4B peptide-mimetic pharmacophoric; robust agent; potential fragment-library; drug-compound; vaccine mimic; annotated properties; oral cancer immunotherapies;

Abstract

BACE-1 is the β-secretase responsible for the initial amyloidogenesis in Alzheimer’s disease, catalyzing hydrolytic cleavage of substrate in a pH-sensitive manner. The catalytic mechanism of BACE-1 requires water-mediated proton transfer from aspartyl dyad to the substrate, as well as structural flexibility in the flap region. Thus, the coupling of protonation and conformational equilibria is essential to a full in silico characterization of BACE-1. In this work, we perform constant pH replica exchange molecular dynamics simulations on both apo BACE-1 and five BACE-1-inhibitor complexes to examine the effect of pH on dynamics and inhibitor binding properties of BACE-1. In our simulations, we find that solution pH controls the conformational flexibility of apo BACE-1, whereas bound inhibitors largely limit the motions of the holo enzyme at all levels of pH. The microscopic pKa values of titratable residues in BACE-1 including its aspartyl dyad are computed and compared between apo and inhibitor-bound states. Changes in protonation between the apo and holo forms suggest a thermodynamic linkage between binding of inhibitors and protons localized at the dyad. Utilizing our recently developed computational protocol applying the binding polynomial formalism to the constant pH molecular dynamics (CpHMD) framework, we are able to obtain the pH-dependent binding free energy profiles for various BACE-1-inhibitor complexes. Our results highlight the importance of correctly addressing the binding-induced protonation changes in protein-ligand systems where binding accompanies a net proton transfer. This work comprises the first application of our CpHMD-based free energy computational method to protein-ligand complexes and illustrates the value of CpHMD as an all-purpose tool for obtaining pH-dependent dynamics and binding free energies of biological systems.Quantum Key Distribution with Qubit Pairs An in silico annotated drug discovery interactive approach for 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.Conformational Dynamics and Binding Free Energies of Inhibitors of BACE-1: From the Perspective of Protonation Equilibria.We propose a new Quantum Key Distribution method in which Alice sends pairs of qubits to Bob; each is in one of four possible states. Bob uses one qubit to generate a secure key and the other to generate an auxiliary key. For each pair he randomly decides which qubit to use for which key. The auxiliary key has to be added to Bob’s secure key in order to match Alice’s secure key. This scheme provides an additional layer of security over the standard BB84 protocol.Keywords: Quantum Key Distribution, Quantum Cryptography1. 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, an in silico annotated drug discovery interactive approach of Inhibitors of BACE-1 of tumor-associated macrophages by a computer-aided designed canditate druggable Toll-like receptor (Pam2IDG) peptide-domain targeted by a pharmacophoric mimetic agonistic agent by Conformational Dynamics Quantum Key Distribution with Qubit Pairs and Binding Free Energies From the Perspective of Protonation Equilibria.

Keywords

Toll-likereceptor;agonist-conjugated;peptide-mimetic;pharmacophoric;multi-targeted, Quantum Key Distribution;Qubit Pairs; in silico; annotated drug discovery; interactive approach; tumor-associated macrophages; computer-aided; druggable; Toll-like receptor; (Pam2IDG) peptide-domain; Conformational Dynamics; Binding Free Energies; Inhibitors of BACE-1; From the Perspective of Protonation Equilibria; Conformational

Abstract

An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate. In this study, we present an Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization Statistical Mechanics for Weak Measurements and Quantum Inseparability Novel procedure Computational Scaffolding on tumorigenic stem cell bacterial infected hybrids for the in silico rescaffolding and side-chain optimization on the neutrophil immune defense CAP37 protein.

Keywords

Improved Quantum-Behaved; Particle Swarm; Optimization Algorithm; Elitist Breeding; Unconstrained Optimization;Statistical Mechanics; Weak Measurements; Quantum Inseparability;Novel procedure; Computational Scaffolding; tumorigenic stem cell bacterial; infected hybrids; silico rescaffolding; side-chain optimization; neutrophil immune defense; CAP37 protein;