Abstract

Glycoprotein-96, a non-polymorphic heat-shock protein, associates with intracellular peptides. Autologous tumor-derived heat shock protein-peptide complex 96 (HSPPC-96) can elicit potent tumor-specific T cell responses and protective immunity in animal models. Chemokines were described originally in the context of providing migrational cues for leukocytes. They are now known to have broader activities, including those that favor tumor growth. Treatment with autologous tumor-derived HSPPC-96 was feasible and safe at all doses tested. Observed immunological effects and antitumor activity were modest, precluding selection of a biologically active dose. Coevolution between proteins is crucial for understanding protein-protein interaction. Simultaneous changes allow a protein complex to maintain its overall structural-functional integrity. In this Research Scientific Project, we combined statistical coupling analysis (SCA) and molecular dynamics simulations on thecomplex-96 (HSPPC-96) protein complex to evaluate coevolution between conserved binding protein domain regions. We reconstructed an inter-protein residue coevolution network, consisting of 37 residues and 37complex-96 (HSPPC-96) binding domains conserved peptide derived residues and its fitness scoring reverse ligand docking interactions. It shows that most of the coevolved residue pairs are spatially proximal. When the mutations happened, the stable local structures were broken up and thus the protein interaction was decreased or inhibited, with a following increased risk of melanoma. The identification of inter-protein coevolved residues in thecomplex-96 (HSPPC-96) complex can be helpful for designing protein drug target and in silico discovery of engineering novel nanomolecule experiments. In this scientific study we have in silico discovered a Unique Small Molecule Modulator of CXCR4 tumor-derived heat-shock protein peptide complex-96 (HSPPC-96) by identifying Hits of Quantum Navigation and Ranking Complex Networks for the in silico development of a unique fragment poly-pharmacologic modulator of CXCR4 tumor-derived heat-shock GGHFGPFDY peptide mimotopic complex-96 (HSPPC-96) by highthroughput identifying hits on the hydrophobic autotaxin/lysophospholipase D pocket.

Keywords

Quantum Navigation; Ranking in Complex Networks; in silico; fragment; poly-pharmacologic; modulator; CXCR4 tumor-derived; heat-shock; GGHFGPFDY peptide; mimotopic complex-96; (HSPPC-96); highthroughput identifying hits; hydrophobic autotaxin/lysophospholipase D pocket.

Abstract

The quantum stochastic differential equation derived from the Lindblad form quantum master equation is investigated. The general formulation in terms of environment operators representing the quantum state diffusion is given. The numerical simulation algorithm of stochastic process of direct photodetection of a driven two-level system for the predictions of the dynamical behavior is proposed. The effectiveness and superiority of the algorithm are verified by the performance analysis of the accuracy and the computational cost in comparison with the classical Runge-Kutta algorithm. A conditionally replicative adenovirus is a novel anticancer agent designed to replicate selectively in tumor cells. However, a leak of the virus into systemic circulation from the tumors often causes ectopic infection of various organs. Therefore, suppression of naive viral tropism and addition of tumor-targeting potential are necessary to secure patient safety and increase the therapeutic effect of an oncolytic adenovirus in the clinical setting. It has also recently been developed a direct selection method of targeted vector from a random peptide library displayed on an adenoviral fiber knob to overcome the limitation that many cell type-specific ligands for targeted adenovirus vectors are not known. In previous studies it has also been further examined whether the addition of a tumor-targeting ligand to a replication-competent adenovirus ablated for naive tropism enhances its therapeutic index. Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates. Novel methodologies are described for the analysis of multiple short linear motif like peptide structures of a protein-drug active binding conserved site. Statistical approaches that rely upon residue equivalence, but not superposition, are developed as chemogenomic informatic tasks can be performed includinig the identification of hinge regions, allosteric conformational changes and transient binding sites. Here, we have for the first time discovered an in silico rational Simulation of Quantum Dynamics Differential Equation Based on the Quantum Stochastic designed of a adenovirus library displaying random viral naive tropism replication-competent Oncolytic virus peptide-mimic pharmacophoric ligand supressor with potential therapeutic properties for pancreatic cancer.

Keywords

Simulation of Quantum Dynamics; Differential Equation; Quantum Stochastic in silico rational; adenovirus library; random peptide-mimic; pharmacophoric ligand supressor; viral naive tropism; replication-competent; Oncolytic virus; therapeutic properties; pancreatic cancer.

Abstract

We propose a method to construct universal order parameters for quantum phase transitions in many-body lattice systems. The method exploits the H-orthogonality of a few near-degenerate lowest states of the Hamiltonian describing a given finite-size system, which makes it possible to perform finite-size scaling and take full advantage of currently available numerical algorithms. An explicit connection is established between the fidelity per site between two H-orthogonal states and the energy gap between the ground state and low-lying excited states in the finite-size system. The physical information encoded in this gap arising from finite-size fluctuations clarifies the origin of the universal order parameter. We demonstrate the procedure for the one-dimensional quantum formulation of the q-state Potts model, for q = 2, 3, 4 and 5, as prototypical examples, using finite-size data obtained from the density matrix renormalization group algorithm. 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 disxovered a Finite-Size Universal Order Parameters and Quantum Phase computer simulated gp100 Peptide mimic Transitions on improved Algorithm for Chemically Tractable Semi-Automated Protein Inhibitor designed pharmacophore as a Vaccine-like and Interleukin-2 in silico generated superagonist with potential clinical effect in Patients with Advanced Melanoma.

Keywords

Universal Order Parameters and Quantum Phase Transitions: A Finite-Size Approach
A 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.

Abstract

Saint Louis encephalitis virus, a member of the $aviviridae subgroup, is a culex mosquito-borne pathogen. Despite severe epidemic outbreaks on several occasions, not much progress has been made with regard to an epitope-based vaccine designed for Saint Louis encephalitis virus. Covalent binding is an important mechanism for many drugs to gain its function. Computational algorithms to model this chemical event and extended it to a web server have been previously generated. The CovalentDock Cloud provides a simple yet user-friendly web interface to perform covalent docking experiments and analysis online. The web server accepts the structures of both the ligand and the receptor uploaded by the user or retrieved from online databases with valid access id. It identifies the potential covalent binding patterns, carries out the covalent docking experiments and provides visualization of the result for user analysis.These novel hyperstructures were generated by Using the BiogenetoligandorolTM AND the CovalentDock Cloud: a web server for automated covalent docking. 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. Here, in Biogenea we have discovered for the first time an Improved Computational Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization to Design an Epitope-Based Mimo-Peptidic hyper agonist consisting of linked active Pharmacophoric chemo-Scaffolds comprising in silico demonstrated vaccine-like potential properties against Saint Louis Encephalitis Virus conserved binding domains.

Keywords

Improved Quantum-Behaved; Particle Swarm; Optimization Algorithm; Elitist Breeding; Unconstrained Optimization; Computational Assay; Design an Epitope-Based; Mimo-Peptidic; hyper agonist; inked active Pharmacophoric; chemo-Scaffolds; in silico; vaccine-like; potential properties; Saint Louis Encephalitis ;Virus conserved; binding domains.

Abstract

Quantum information processing devices need to be robust and stable against external noise and internal imperfections to ensure correct operation. In a setting of measurement-based quantum computation, we explore how an intelligent agent endowed with a projective simulator can act as controller to adapt measurement directions to an external stray field of unknown magnitude in a fixed direction. We assess the agent’s learning behavior in static and time-varying fields and explore composition strategies in the projective simulator to improve the agent’s performance. We demonstrate the applicability by correcting for stray fields in a measurement-based algorithm for Grover’s search. Thereby, we lay out a path for adaptive controllers based on intelligent agents for quantum information tasks. Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Targeting pathogenic T cells using vaccines consisting of synthetic peptides representing T cell epitopes is one such strategy that is currently being evaluated with encouraging results. Future challenges in the development of therapeutic vaccines include selection of appropriate antigens and peptides, optimization of peptide dose and route of administration and identifying strategies to induce bystander suppression. Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given peptide based on their structural complementarity. Compared to other peptide and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Peptidomimetics, deriving from structure-based, combinatorial or protein dissection approaches, can play a key role as hit compounds. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets. Here, in Biogenea we have for the first time generated a surface representation adaptive quantum computation in changing environments using projective IHMVYSK peptide-mimo based chemo-ligand simulation designed of therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications.

Keywords

Adaptive quantum computation in changing environments using projective simulation A surface representation designed IHMVYSK peptide-mimo based chemo-ligand comprising therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications.

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. In silico Drug discovery and development of novel multi-target molecules is an interdisciplinary, expensive and time-consuming procedure. Computer aided drug discovery advancements during the past decades have improved the way of pharmaceutical research design of novel bioactive huper-structured drug-gable molecules. Computer aided drug design helps in reducing the cost and time for drug discovery process which otherwise takes many years. Virtual screening and docking studies helped to obtain ligand molecules that can inhibit the important Proteins involved in the pathogenesis of Ebola virus. It is noticed that the chemical compounds might be the promising candidates drug-like small targeted compounds for further pre-clinical and clinical investigation, and that the NP and the octapeptides ATLQAIAS and ATLQAENV, as well as AVLQSGFR, might be pre-clinically translated and antisense converted to effective direct inhibitors against the Ebola Virus Fusion Conserved Proteoma. Meanwhile, we in silico generated conserved octapeptides mimotopic pharmaco-ligands based on the “distorted key energy binding fitness scoring” theory to in-silico anti-sense peptides by in-silico translate them and transform them into a scaffold energy hopping structure in order to design potent selective super-agonsist anti-peptide poly-mimic new superstructure which is explicitly elucidated. We also combined all existing methods for computational huper-structured drug design methodologies to induce catalysis of Ebola Virus EBOV NP and EBO16 peptides by inducing energetically targeted favorable hydrogen bonds, van der Waals, and electrostatic interactions to a high-energy reaction conserved motif-based transition state(s) and/or intermediate(s) of Ebola virus. In this present Research Scientific Project , for first time we developed a computational method for designing motif-like conserved residues and ligand binding virus proteins with two properties characteristic of naturally occurring binding sites in addition to specific energetically favorable interactions with our newly designed Distribution of quantum Ligand based predictions of a virion-attached pharmacophore cross-reacting asymmetric cloning synthetic Fisher information machines on EQHHRRTDN/GAAIGLAWIPYFGPAA peptide mimetic ligand modeling potential therapeutic properties against conserved conserved EBO16 over-expressed regions Ebola virus.

Keywords

Distribution of quantum Fisher information; asymmetric cloning machines; Ligand based prediction; virion-attached pharmacophore; cross-reacting; synthetic EQHHRRTDN/GAAIGLAWIPYFGPAA; peptide mimetic; therapeutic properties; Ebola virus; conserved EBO16; over-expressed regions.

Abstract

Molecular dynamics (MD) simulation is an emerging in silico technique with potential applications in diverse areas of pharmacology. Over the past three decades MD has evolved as an area of importance for understanding the atomic basis of complex phenomena such as molecular recognition, protein folding, and the transport of ions and small molecules across membranes. The application of MD simulations in isolation and in conjunction with experimental approaches have provided an increased understanding of protein structure-function relationships and demonstrated promise in drug discovery. AT-101, a small molecule inhibitor of anti-apoptotic Bcl-2 family members, activates the SAPK/JNK pathway and enhances radiation-induced apoptosis. C-Met inhibitor MK-8003 radiosensitizes c-Met-expressing non-small-cell lung cancer cells with radiation-induced c-Met-expression. Nutlin-3 radiosensitizes hypoxic prostate cancer cells independent of p53. C-Met Inhibitor MK-8003 Radiosensitizes c-Met-Expressing Non-Small Cell Lung Cancer Cells with Radiation-Induced c-Met-Expression. In this study we for the first time designed small-molecule PUMA derived peptide mimetic inhibitors for mitigating a potential radiation-induced cell death. These chemical recored scaffolds are consisting of linked small pharmaco-fragments and DNA-induced nucleic acid mimicking molecules that may interact with the DNA double-strand breaks (called Dbait) and would possible in the future act as a disorganizing damage signaling and DNA repair druggable compound. We in silico analyzed the fitness scoring results and the pharmaco-docking free energy binding effects of our synthetic mimotopic Dbait lignads in conserved DNA mutant regions responsible for the tumor growth and performed preliminary ligand structure based QSAR studies of their mechanism(s) of action. Here, in Biogenea we finally in silico multi-molecularly targeted conserved Radiosensitization regions of Human Cancer binding domains by Modulating Inhibitor of apoptosis purpose for the potentiating of a future enhanced DNA repair activity which is often associated with tumor resistance to radiotherapy. Although many radiosensitizers have been developed, their clinical benefit is hampered by a failure to improve the therapeutic ratio due to a lack of tumor specific delivery over normal tissue. We propose to utilize drug conjugated activatable cell penetrating peptides (ACPP) as tumor selective delivery vehicles for the in silico of a fragment ligand based novel multitargeted potent radiosensitizers. Cyclic RGD pretargeted ACPP (ACPP-RGD) are selectively cleaved and activated in the tumor microenvironment through tumor associated matrix metalloproteinase activity and RGD binding integrins utilising Molecular dynamics in silico drug discovery simulations from structure function relationships to a small-molecule PUMA targeted ACPP (ACPP-RGD) peptide mimotopic hyper-Inhibitory ligand pocket binding as a potent pharmacoregulator comprising potential mitigation activities of the radiation-Induced cell death.

Keywords

Molecular dynamics simulations: structure function relationships; ligand pocket binding; in silico; drug discovery; small-molecule; PUMA targeted; ACPP (ACPP-RGD) peptide; mimotopic; hyper-Inhibitory; potent pharmacoregulator; potential activities; mitigation; radiation-Induced cell death; Molecular dynamics simulations, Cytochrome P450, Drug-drug interactions, Genetic polymorphism, Drug design, Allosteric binding sites, Cryptic binding sites

Abstract

We investigate Quantum Darwinism and the emergence of a classical world from the quantum one in connection with the spectral properties of the environment. We use a microscopic model of quantum environment in which, by changing a simple system parameter, we can modify the information back flow from environment into the system, and therefore its non-Markovian character. Quantum Darwinism is a fascinating theory that explains the emergence of a classical objective reality in terms of proliferation of information about certain states of a quantum system into the environment1,2. We live in a quantum Universe, the behaviour of all microscopic constituents being described by the laws of quantum physics. There is overwhelming evidence that this incredibly successful theory applies at all scales. Why then the macroscopic objects populating our everyday reality are only found in a much smaller subset of states, consistent with classical laws? We show that the presence of memory effects hinders the emergence of classical objective reality, linking these two apparently unrelated concepts via a unique dynamical feature related to decoherence factors.: HCV infection has been declared as a principal health problem in more than 200 million individuals throughout the world. It is a positive-stranded RNA virus and classified as a hepacivirus of the flaviviridae family. Unlike other viral infections Hepatitis C Virus even with its high replication rate can stick within a human host for decades without any irritation or liver damage. Estimated 10 million people are believed to be infected by HCV alone in Pakistan. Eventually the infection causes severe complications in 60 to 70% of patients such as cirrhosis, fibrosis, liver failure and hepatocellular carcinoma. Prior to the development of HCV protease inhibitors combination therapy, patients with HCV infection were treated with pegylated interferon-α and ribavirin. The adverse side effects associated with this type of treatment such as anemia, flu-like symptoms, depression, gastrointestinal symptoms, fatigue and cutaneous reactions may lead to the discontinuation of treatment in certain number of patients. The growth in scientific knowledge of HCV life cycle and its replication leads to the development of inhibitors of HCV proteases. A polyprotein precursor encoded by HCV RNA genome containing structural proteins capsid (C), membrane (prM), envelope (E) and nonstructural (NS) proteins (NS1, NS2a, NS2b, NS3, NS4a, NS4b, NS5). NS3 protease when activated by NS4A causes the cleavage of polyprotein producing the non-structural proteins 4A, 4B, 5A, 5B and is thus very supportive in the replication of virus. That is why NS3/4A protease is a significant emerging target for the treatment of HCV infection. NS3 associates to the ER membrane only in the presence of NS4A. Main actively conserved protein target families can be distinguished by a simple look at physicochemical properties (molecular weight, log P, polar surface area, H-bond donor and acceptor counts) of their cognate ligands (Morphy, 2006). One can thus easily imagine that more sophisticated descriptors can be used to predict a global target profile for any given compound, provided that targets to be predicted are sufficiently well described by existing ligands. In this study we resulted finally in a Non-Markovianity In Silico generation of a hinders Quantum Darwinism sophisticated descriptor for the in silico identification and free energy evaluation of hybrid KPQRKTKRNT peptidomimetic leads and for the potential simultaneous inhibition of helicase and HCV Structural NS3/4A protease regions.

Keywords

Non-Markovianity hinders Quantum DarwinismIn Silico generation of a sophisticated descriptor for the in silico identification and free energy evaluation of hybrid KPQRKTKRNT peptidomimetic leads for a potential, simultaneous inhibition of helicase and HCV´sStructural NS3/4A protease regions.

Abstract

Complex networks are formal frameworks capturing the interdependencies between the elements of large systems and databases. This formalism allows to use network navigation methods to rank the importance that each constituent has on the global organization of the system. A key example is Pagerank navigation which is at the core of the most used search engine of the World Wide Web. Inspired in this classical algorithm, we define a quantum navigation method providing a unique ranking of the elements of a network. We analyze the convergence of quantum navigation to the stationary rank of networks and show that quantumness decreases the number of navigation steps before convergence. In addition, we show that quantum navigation allows to solve degeneracies found in classical ranks. By implementing the quantum algorithm in real networks, we confirm these improvements and show that quantum coherence unveils new hierarchical features about the global organization of complex systems. The search for information in the World Wide Web (WWW) through search engines has turned into a daily habit and an essential tool to fulfill most of our work duties. An ideal search engine looks for the information the user is querying amongst billions of webpages in real time, and produces a ranking of the results sorted according the user expectations. Although not being among the first search engines available, the Google search engine was the first to achieve these goals efficiently, establishing one of the milestones of the digital era. Its main novelty was to classify and rank webpages based on the interrelations created between them through the hyperlinks1, rather than using only their intrinsic features (such as the page content). Google’s ranking algorithm, known as Pagerank2 (PR), is rooted in a diffusion process that mimics the user’s navigation through webpages as the motion of a random walker following a shannon entropy Quantum Navigation and Ranking hyperlink pathways. in Complex Networks descriptors (SHED) for the in silico prediction of an annotated suitable lead chemo-recored compound as a potent computer predicted inhibitor comprising potential hyper-mimicking activities to 5 conserved anti-plasmodium peptides.

Keywords

Quantum Navigation; Ranking in Complex Networks; shannon entropy descriptor; (SHED) for the in silico prediction; annotated suitable; lead chemo-recored compound; potent computer predicted inhibitor; hyper-mimicking activities; 5 conserved; anti-plasmodium peptides.

Abstract

A quantum analog of the computational complexity theory has been developed [7]–[8], with the introduction of complexity classes of easy and hard problems, the notion of difficulty being now with respect to the number of required operations on a quantum, instead of classical, computer. A new formulation of monotonically convergent algorithms which allows to optimize both the control duration and the field influence has been presented [9]. They apply this algorithm to the control of spin systems in Nuclear Magnetic Resonance and show how to implement CNOT gates in systems of two and four coupled spins. Also, a new formulation of quantum algorithm which allows to distribute amplitudes over two copies of small quantum subsystems has been proposed [10], where a standard algorithm designs a new method of a fixed number of copies and applied to the control of multi-qubit system. Keloids result from aberrations in the normal wound healing cascade and can lead to pruritus, contractures and pain. The underlying mechanisms of excessive scarring are not yet understood, and most therapeutic strategies remain unsatisfactory. Psoriasin (S100A7) and koebnerisin (S100A15) are released by keratinocytes during physiological wound healing. Psoriasin (S100A7) and koebnerisin (S100A15) are released by keratinocytes during physiological wound healing. S100 production is markedly decreased in keloid scar tissue. The disturbed epidermal S100 expression might contribute to keloid formation; thus, it has been previously studied their effect on dermal fibroblasts and extracellular matrix (ECM) production. Here, in Biogenea Pharmaceuticals Ltd we discovered for the first time the GENEA-AntiPsorerisin-10715. An in silico rational computer-aided designed of Antimicrobial Peptides Psoriasin (S100A7) and Koebnerisin (S100A15) mimetic pharmacophore for the Suppression of the Extracellular Matrix Production and Proliferation of Human Fibroblasts by Predicting interacting residues using long-distance information for the Collapsing a Perfect Superposition to a Chosen Quantum State without Measurements on in silico rational computer-aided design to Antimicrobial Peptide-mimetic Psoriasin (S100A7) and Koebnerisin (S100A15) high binding free energy pharmacophoric hyper-scaffolds as a novel synthetic pharmaco-ligand with potential inhibitory activities for the Suppression of the Extracellular Matrix Production and Proliferation of Human Fibroblasts in novel decoding hidden Markov models.

Keywords

Collapsing a Perfect Superposition; Chosen Quantum State; Measurement; rational computer-aided designed; Antimicrobial Peptide-mimetic; Psoriasin (S100A7); Koebnerisin; (S100A15); high binding free energy; pharmacophoric; hyper-scaffolds; novel synthetic pharmaco-ligand; potential inhibitory activities; Suppression of the Extracellular Matrix; Production; Proliferation of Human Fibroblasts.