Abstract

Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to up-regulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such “high-resolution” detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields by designing the Sniper: Improving Targeted Human Cytolytic Fusion Proteins for Anti-Cancer Therapy via Molecular Simulation of an in silico KIF20A-derived Peptide agonistic mimicking sited designed poly-chemo-scaffold as an innovative drug-like molecule with potential clinical hyper-inhibitor properties in Gemcitabine treated Patients With Advanced Pancreatic Cancer.

Keywords

immunotherapy, targeted human cytolytic fusion proteins, molecular dynamics, high performance computing, Angiogenin, Granzyme B; Designing the Sniper: Targeted Human Cytolytic Fusion Proteins; Anti-Cancer Therapy; Molecular Simulation; in silico; KIF20A-derived; Peptide agonistic; mimicking sited; poly-chemo-scaffold; innovative drug-like; clinical hyper-inhibitor properties; Gemcitabine; Patients With Advanced Pancreatic Cancer.

Abstract

This work presents a few variational multiscale models for charge transport in complex physical, chemical and biological systems and engineering devices, such as fuel cells, solar cells, battery cells, nanofluidics, transistors and ion channels. An essential ingredient of the present models, introduced in an earlier paper (Bulletin of Mathematical Biology, 72, 1562-1622, 2010), is the use of differential geometry theory of surfaces as a natural means to geometrically separate the macroscopic domain from the microscopic domain, meanwhile, dynamically couple discrete and continuum descriptions. Our main strategy is to construct the total energy functional of a charge transport system to encompass the polar and nonpolar free energies of solvation, and chemical potential related energy. By using the Euler-Lagrange variation, coupled Laplace-Beltrami and Poisson-Nernst-Planck (LB-PNP) equations are derived. The solution of the LB-PNP equations leads to the minimization of the total free energy, and explicit profiles of electrostatic potential and densities of charge species. To further reduce the computational complexity, the Boltzmann distribution obtained from the Poisson-Boltzmann (PB) equation is utilized to represent the densities of certain charge species so as to avoid the computationally expensive solution of some Nernst-Planck (NP) equations. Consequently, the coupled Laplace-Beltrami and Poisson-Boltzmann-Nernst-Planck (LB-PBNP) equations are proposed for charge transport in heterogeneous systems. A major emphasis of the present formulation is the consistency between equilibrium LB-PB theory and non-equilibrium LB-PNP theory at equilibrium. Another major emphasis is the capability of the reduced LB-PBNP model to fully recover the prediction of the LB-PNP model at non-equilibrium settings. To account for the fluid impact on the charge transport, we derive coupled Laplace-Beltrami, Poisson-Nernst-Planck and Navier-Stokes equations from the variational principle for chemo-electro-fluid systems. A number of computational algorithms is developed to implement the proposed new variational multiscale models in an efficient manner. A set of ten protein molecules and a realistic ion channel, Gramicidin A, are employed to confirm the consistency and verify the capability. Extensive numerical experiment is designed to validate the proposed variational multiscale models. A good quantitative agreement between our model prediction and the experimental measurement of current-voltage curves is observed for the Gramicidin A channel transport. This paper also provides a brief review of the Variational multiscale Success and Incoherence of Orthodox Quantum Mechanics models for charge transport CHARMM additive and polarizable force fields for biophysics and computer-aided drug design as an in silico KIF20A-derived Peptide agonistic mimicking sited and computer-aided designed poly-chemo-scaffold innovative drug-like molecule with potential clinical hyper-inhibitor properties in Patients With Advanced Pancreatic Cancer.

Keywords

Variational multiscale Success; Incoherence of Orthodox Quantum Mechanics; models for charge transport; CHARMM additive; polarizable force fields; biophysics and computer-aided drug design; in silico; KIF20A-derived; Peptide agonistic; mimicking sited; computer-aided designed; poly-chemo-scaffold; innovative drug-like molecule; clinical hyper-inhibitor properties; Patients With Advanced Pancreatic Cancer; Variational multiscale models, Ion channels, Fuel cells, Nanofluidics, Electronic devices, Laplace-Beltrami equation, Poisson-Boltzmann equation, Nernst-Planck equation, Navier-Stokes equation.

Abstract

Geometric modeling of biomolecules plays an essential role in the conceptualization of biolmolecular structure, function, dynamics and transport. Qualitatively, geometric modeling offers a basis for molecular visualization, which is crucial for the understanding of molecular structure and interactions. Quantitatively, geometric modeling bridges the gap between molecular information, such as that from X-ray, NMR and cryo-EM, and theoretical/mathematical models, such as molecular dynamics, the Poisson-Boltzmann equation and the Nernst-Planck equation. In this work, we present a family of variational multiscale geometric models for macromolecular systems. Our models are able to combine multiresolution geometric modeling with multiscale electrostatic modeling in a unified variational framework. We discuss a suite of techniques for molecular surface generation, molecular surface meshing, molecular volumetric meshing, and the estimation of Hadwiger’s functionals. Emphasis is given to the multiresolution representations of biomolecules and the associated multiscale electrostatic analyses as well as multiresolution curvature characterizations. The resulting fine resolution representations of a biomolecular system enable the detailed analysis of solvent-solute interaction, and ion channel dynamics, while our coarse resolution representations highlight the compatibility of protein-ligand bindings and possibility of protein-protein interactions.In silico discovery of a Asn-Ile-Ile-Gly-Val-Ser-Tyr peptide mimetic high free energy recored chemical analog molecule CFTR targeted binding sites as a future mutant corrector against over-expressed cystic fibrosis pathological post-transcripts.Multiscale geometric modeling of macromolecules II: Lagrangian representation.Abstract: Cystic Fibrosis (CF) is the most common lethal autosomal recessive disorder in Caucasia population, affecting approximately 30,000 people in the United States and ∼70,000 worldwide. While there is yet no cure for CF, aggressive treatment including mucus thinners, antibiotics, anti-inflammatories and bronchodilators along with physical therapy and proper nutritional repletion, can lengthen and improve the quality of life of CF patients. Peptides derived from mutant CFTR protein which inhibit intracellular degradation and/or retention of mutant CFTR proteins have been clinially used. Methods of inhibiting intracellular degradation and/or retention of mutant CFTR protein by administering peptides having an amino acid sequence corresponding to mutant CFTR amino acid sequences have also been reported in other studies. Further, methods of preventing cellular retention and degradation of an otherwise membrane bound protein by competitively inhibiting intracellular degradation (proteolysis) and retention which would otherwise retain or degrade synthesized mutant proteins prior to arrival of the protein at the cell surface have previously been tested. In our project we conducted a fragment-ligand based structure drug discovery procedure through a ligand-based high throughput screening of 150,000 chemically diverse compounds and of more than 1,500 analogs of active compounds yielded several classes of CFTR corrector multi-targeted to the conserved cystic fibrosis over-expressed nucleic acid binding sites mutant domains. Previous biochemical studies also suggested a mechanism of action involving improved CFTR folding at the ER increased stability at the cell surface. Previous reffered biologically active peptides have been used to inhibit intracellular degradation (proteolysis) and/or retention processes to treat or cure Cystic Fibrosis disease. Peptides are short-lived and typically involve short amino acid stretches bearing few “hot spots”, thus the identification of molecules able to mimic them may produce important lead compounds for the treatment of CF. Here, we have for the first time discovered Multiscale geometric Lagrangian representation modeling of an in silico discovery of a Asn-Ile-Ile-Gly-Val-Ser-Tyr peptide mimetic high free energy recored chemical analog molecule CFTR targeted binding sites as a future mutant corrector against over-expressed cystic fibrosis pathological post-transcripts.

Keywords

In silico discovery; Asn-Ile-Ile-Gly-Val-Ser-Tyr peptide mimetic; high free energy; recored chemical analog molecule; CFTR targeted; binding sites; future mutant corrector; over-expressed; cystic fibrosis; pathological post-transcripts; Multiscale geometric modeling; Lagrangian representation, variational multiscale modeling, Multiresolution surface, Energy functional, Meshing, Curvature, Electrostatics.

Abstract

We present a scheme for a quantum optical implementation of Grover’s algorithm based on resonant atomic interactions with classical fields and dispersive couplings with quantized cavity fields. The proposed scheme depends on preparation of entangled states and is within current state-of-the-art technology. As was first shown by Grover (1), search of a database by using quantum mechanics can be substantially faster than any classical search of unsorted data. For example, it was shown by Grover that, by using quantum superpositions and quantum entanglement, we can find an object in an unsorted database containing N objects in O() quantum mechanical steps instead of O(N) steps (1–3).Abstract: 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 of a Quantum optical implementation of Grover’s algorithm 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.

Keywords

In Silico, sophisticated descriptor, in silico identification, free energy evaluation, hybid, peptidomimetic leads, simultaneous inhibition, helicase, HCV´s Structural NS3/4A protease regions, Quantum optical implementation, Grover’s algorithm, Quantum optical implementation of Grover’s algorithm;In Silico generation; sophisticated descriptor; hybrid KPQRKTKRNT; peptidomimetic leads; for a potential, simultaneous inhibition; helicase;HCV Structural NS3/4A protease regions.

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. 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. 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. Anti-cytotoxic T-lymphocyte antigen-4 (CTLA-4) antibodies, such as ipilimumab, have generated measurable immune responses to Melan-A, NY-ESO-1, and gp100 antigens in metastatic melanoma. Vaccination against such targets has potential forimmunogenicity and may produce an effector memory T-cell response. It has been previously determined the effect of CTLA-4 blockador on antigen-specific responses following vaccination. In-depth immune monitoring was performed on three ipilimumab-treated patientsprevaccinated with gp100 DNA (IMF-24), gp100209–217 and tyrosinase peptides plus GM-CSFDNA (IMF-32), or NY-ESO-1 protein plus imiquimod (IMF-11). In previous studies it was shown that peripheral blood mononuclearcells were analyzed by tetramer and/or intracellular cytokine staining following 10-day culturewith HLA-A*0201-restricted gp100209–217 (ITDQVPFSV), tyrosinase369–377 (YMDGTMSQV),or 20-mer NY-ESO-1 overlapping peptides, respectively. It has also been evaluated on the PDBbind v2012 core set where istar platform combining with RF-Score manages to reproduce Pearson’s correlation coefficient and Spearman’s correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. Here, we have discovered for the first time a variational eigenvalue solver on a photonic quantum processor of algorithms for Large-Scale Protein-Ligand Docking experiments for the in silico prediction of a computer-aided molecular designed CTLA-4 blockador for the increasement of the antigen-specific CD8+ T-cells to the inprevaccinated patients with melanoma.

Keywords

variational eigenvalue solver, photonic quantum processor, in silico, computer-aided molecular, CTLA-4 blockador, increasement, antigen-specific CD8+, T-cells, inprevaccinated patients, melanoma, new cluster of algorithms, Web Platform, Large-Scale, Protein-Ligand, Docking experiments

Abstract

In mathematical physics and psychology, “quantum decision theory” has been proposed to explain anomalies in human decision-making. One of such quantum models has been proposed to explain time inconsistency in human decision over time. In this study, we conducted a behavioral experiment to examine which quantum decision models best account for human intertemporal choice. We observed that a q-exponential model developed in Tsallis’ thermodynamics (based on Takahashi’s (2005) nonlinear time perception theory) best fit human behavioral data for both gain and loss, among other quantum decision models. In this study, we conducted an Experimental Comparison of Quantum Decision Theoretical Models of Intertemporal Choice for Gain and Loss Protein−Ligand Hyper drug-target Complexes interaction analysis for the in silico free energy potency optimization for the in silico discovery of a poly-targeted binding-pocket peptide mimic annotated chemo-antagonists to HIV-II viral replication cycle associated enzymes.

Keywords

An Experimental Comparison of Quantum Decision Theoretical Models of Intertemporal Choice for Gain and Loss Protein−Ligand Hyper drug-target Complexes interaction analysis for the in silico free energy potency optimization for the in silico discovery of a poly-targeted binding-pocket peptide mimic annotated chemo-antagonists to HIV-II viral replication cycle associated enzymes, Discounting; Neuroeconomics; Econophysics; Quantum Decision Theory;

Abstract

Recently, it is shown that there is a crucial contradiction within von Neumann’s theory [K. Nagata and T. Nakamura, Int. J. Theor. Phys. 49, 162 (2010)]. We derive a proposition concerning a quantum expected value under the assumption of the existence of the directions in a spin-1/2 system. The quantum predictions within the formalism of von Neumann’s projective measurement cannot coexist with the proposition concerning the existence of the directions. Therefore, we have to give up either the existence of the directions or the formalism of von Neumann’s projective measurement. Hence, there is a crucial contradiction within von Neumann’s theory. We discuss that this crucial contradiction makes the theoretical formulation of Deutsch’s algorithm questionable. Especially, we systematically describe our assertion based on more mathematical analysis using raw data. Our discussion, here, improves Can Von Neumann’s Quantum Computation Theory on Protein−Ligand Hyper drug-target Complexes interaction analysis for the in silico free energy potency optimization of a poly-targeted binding-pocket peptide mimic annotated chemo-antagonists to HIV-II viral replication cycle associated enzymes.

Keywords

Can Von Neumann’s Theory; Quantum Computation; Protein−Ligand; Hyper drug-target Complexes; interaction analysis; in silico; free energy; potency optimization; poly-targeted; binding-pocket; peptide mimic; annotated chemo-antagonists; HIV-II viral replication; cycle associated enzymes, Quantum Measurement Theory, Quantum Computer, Formalism, Subject Areas: Applied Physics

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 scoring function presented here, used either on its own or in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.A Hyper drug-target interaction analysis for the In silico free energy potency optimization for the in silico discovery of a poly-targeted binding-pocket peptide mimic annotated chemo-antagonists to HIV-II viral replication cycle associated enzymes.NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes. Exploring hyper drug-target interactions using restricted Boltzmann machines. Computational development of rubromycin-based lead compounds for HIV-1 reverse transcriptase inhibition. Considerable success has been achieved in the treatment of HIV-1 infection, and more than two dozen antiretroviral drugs are available targeting several distinct steps in the viral replication cycle. However, resistance to these compounds emerges readily, even in the context of combination therapy. Drug toxicity, adverse drug-drug interactions, and accompanying poor patient adherence can also lead to treatment failure. These considerations make continued development of novel antiretroviral therapeutics necessary. Current approaches for designing chemical recored ligand binding proteins for medical and biotechnological uses rely upon raising antibodies against a target antigen in immunized animals and/or performing laboratory directed evolution of proteins with an existing low affinity for the desired ligand, both of which offer incomplete control over molecular details. Computational design has the potential to provide a general, complementary low mass algorithmic approach for small molecule recognition in which designed and predicted features and selectivity can be rationally in sioico programmed. Structural and biophysical characterization of previously designed ligand binding proteins has revealed numerous discrepancies with the design models, however, and it was concluded that protein-ligand interaction design is an unsolved problem. The development of robust computational methods for the design of small molecule-binding proteins with high affinity and selectivity would have wide-ranging applicationS. The goal of existing methods for computational enzyme design is to promote catalysis by creating energetically favorable hydrogen bonding, van der Waals, and electrostatic interactions to a high-energy reaction transition state(s) and/or intermediate(s). Although these interactions are also important for stabilizing the bound ground-state conformations of protein-motif conserved petide mimetic pharmacophore consisting of linked small molecule complexes as the sole determinant of small molecule binding. Here, in this research drug discovery approach we discovered an annotated Neural-Network-Based NNScoring Function Characterization of Protein−Ligand Hyper drug-target Complexes interaction analysis for the in silico free energy potency optimization of a poly-targeted binding-pocket peptide mimic chemo-antagonists to HIV-II viral replication cycle associated enzymes.

Keywords

Neural-Network-Based, NNScoring Function, Protein−Ligand Hyper drug-target Complexes, interaction analysis, in silico, free energy, potency optimization, in silico discovery, poly-targeted, binding-pocket, peptide mimic, annotated chemo-antagonists, HIV-II viral replication, cycle associated enzymes

Abstract

A theory elaborated by the author on revised quantum electrodynamics (RQED) is elucidated in a condensed form on special important points. The latter concerns the basic electromagnetic field equations in a vacuum state, the connection of this state with the Zero Point Energy (ZPE), the procedure of quantization, steady states of particle models, the concept of the individual photon, and examples on experimental support of the theory on Some Elucidations of the Theory on Revised Quantum Electrodynamic on Telomerase Peptide Vaccination simulated poly-chemo mimotopic pharmacological structure Combined with Temozolomide as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site comparison.

Keywords

Some Elucidations; Theory on Revised Quantum Electrodynamic; Telomerase Peptide Vaccination; simulated poly-chem mimotopic pharmacological structure Combined with Temozolomide as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site comparison, Quantum Electrodynamics, Zero Point Energy, Standard Model and Beyond.

Abstract

The existing certificateless signcryption schemes were designed mainly based on the traditional public key cryptography, in which the security relies on the hard problems, such as factor decomposition and discrete logarithm. However, these problems will be easily solved by the quantum computing. So the existing certificateless signcryption schemes are vulnerable to the quantum attack. Multivariate public key cryptography (MPKC), which can resist the quantum attack, is one of the alternative solutions to guarantee the security of communications in the post-quantum age. Motivated by these concerns, we proposed a new construction of the certificateless multi-receiver signcryption scheme (CLMSC) based on MPKC. The new scheme inherits the security of MPKC, which can withstand the quantum attack. Multivariate quadratic polynomial operations, which have lower computation complexity than bilinear pairing operations, are employed in signcrypting a message for a certain number of receivers in our scheme. Security analysis shows that our scheme is a secure MPKC-based scheme. We proved its security under the hardness of the Multivariate Quadratic (MQ) problem and its unforgeability under the Isomorphism of Polynomials (IP) assumption in the random oracle model. The analysis results show that our scheme also has the security properties of non-repudiation, perfect forward secrecy, perfect backward secrecy and public verifiability. Compared with the existing schemes in terms of computation complexity and ciphertext length, our scheme is more efficient, which makes it suitable for terminals with low computation capacity like smart cards.A Telomerase Peptide Vaccination simulated poly-chemo mimotopic pharmacological structure Combined with Temozolomide as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients generated by the BiogenetoligandorolTM based parallel web service for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site comparison.Quantum Attack-Resistent Certificateless Multi-Receiver Signcryption Scheme. immune response, and clinicalresponse in melanoma patients after combined therapy with temozolomide and the telomerase peptide vaccine GV1001 in previous GV1001 trials showed immune responses in approximately 60% of lung orpancreatic cancer patients. Previous Experimental Studies Twenty-five subjects with advanced stage IV melanoma (M1B or M1C) received concomitant temozolomide and GV1001. Temozolomide was administered 200 mg/m2 orally for 5 daysevery fourth week, and GV1001 as eight injections over 11 weeks. Immune response was evaluated bydelayed type hypersensitivity, T-cell proliferation, and cytokine assays. The immunologic responders continued monthly vaccination. Detecting evolutionary relationships across existing fold space, using sequence order-independent profile-profile alignments. Proc. Natl Acad. Sci. USA, 105, 5441–5446]. A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics, 25, i305–i312.]. These algorithms have been extensively benchmarked and shown to outperform most existing algorithms. Moreover, several predictions resulting from SMAP-WS have been validated experimentally. Thus far SMAP-WS has been applied to predict drug side effects, and to repurpose existing drugs for new indications. SMAP-WS provides both a user-friendly web interface and programming API for scientists to address a wide range of compute intense questions in biology and drug discovery. Here, in Biogenea we have for the first generated a for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site comparison Quantum Attack Resistent Certificateless Multi Receiver Signcryption Scheme on Telomerase Peptide Vaccination simulated poly-chemo mimotopic pharmacological structure as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients.

Keywords

A Quantum Attack Resistent Certificateless, Multi Receiver, Signcryption Scheme, Telomerase Peptide Vaccination, simulated poly-chemo mimotopic pharmacological structure, novel in silico promising anti-cancer drug-like agent, Stage IV Melanoma Patients, structural proteome-wide, multi-targeted, ligand-binding, conserved binding, pharmacophoric, site comparison.Quantum Attack-Resistent Certificateless Multi-Receiver Signcryption Scheme