Abstract

A stochastic process’ statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process’ cryptic order–a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost–one trades off prediction for generation complexity. The fertilization process includes a cascade of events that the spermatozoon must undergo before fusing with the oocyte plasma membrane. One of the key steps in the fertilization cascade is the recognition and binding between the complementary molecules present on the spermatozoon and zona pellucida (ZP) of the oocyte. In previous scientific reports using the phase peptide display technique, a novel dodecamer sequence, designated as YLP12, was identified that is involved in sperm-ZP recognition/binding. This synthetic 12-mer peptide based on this sequence and its monovalent Fab′ antibodies specifically and significantly (P <0.05) inhibited human sperm-ZP binding. On the basis of the above findings, the present Project was conducted to take the advance of the previous investigated sperm peptide sequence(s) involved in recognition and binding to the complementary molecule of the ZP in humans for the in silico generation of fragment based biosimilars peptidomimetic pharmacophores. Discovering and describing correlation and pattern are critical to progress in the physical sciences. Observing the weather in California last Summer we find a long series of sunny days interrupted only rarely by rain–a pattern now all too familiar to residents. Analogously, a one-dimensional spin system in a magnetic field might have most of its spins “up” with just a few “down”–defects determined by the details of spin coupling and thermal fluctuations. Though nominally the same pattern, the domains of these systems span the macroscopic to the microscopic, the multi-layer to the pure. Despite the gap, can we meaningfully compare these two patterns? To exist on an equal descriptive footing, they must each be abstracted from their physical embodiment by, for example, expressing their generating mechanisms via minimal probabilistic encodings. Measures of unpredictability, memory, and structure then naturally arise as information-theoretic properties of these encodings. Indeed, the fundamental interpretation of (Shannon) information is as a rate of encoding such sequences. This recasts the informational properties as answers to distinct communication problems. For instance, a process’ structure becomes the problem of two observers, Alice and Bob, synchronizing their predictions of the process. However, what if the communication between Alice and Bob is not classical? What if Alice instead sends qubits to Bob–that is, they synchronize over a quantum channel? Does this change the communication requirements? More generally, does quantum communication enhance our understanding of what “pattern” is in the first place? What if the original process is itself quantum? More practically, is the quantum encoding more compact? A provocative answer to the last question appeared recently1,2,3 suggesting that a quantum representation can compress a stochastic process beyond its known classical limits4. In the following, we introduce a new construction for quantum channels that improves and broadens that result to any memoryful stochastic process, is highly computationally efficient, and points toward optimal quantum compression. Importantly, we draw out the connection between quantum compressibility and process cryptic order–a purely classical property that was only recently discovered5. Finally, we discuss the subtle way in which the quantum framing of pattern and structure differs from the classical Occam’s Quantum Strop: Synchronizing and Compressing Classical Cryptic in silico discovery of a novel identified Human testis-specific 12-mer YLP12 Sperm Peptide (SNR12-YLP12YLPVGGLRRIGG) Consensus17GHRGRRVGLGGGGRIGG) Sequence-based chemoanalogues Involved Processes via a Quantum Channel Gamete Genomic Integrity Effect in Immunocontraception.

Keywords

Gamete Genomic; Integrity Effect;in silico discovery;novel identified; Human Sperm; Peptide; Sequence-based; chemoanalogues;Egg Binding;Immunocontraception, Occam’s Quantum Strop; Synchronizing;Compressing; Classical Cryptic Processes; Quantum Channel; Gamete Genomic; Integrity Effect; in silico discovery; Human testis-specific; 12-mer YLP12; Sperm Peptide; (SNR12-YLP12YLPVGGLRRIGG); Consensus;17GHRGRRVGLGGGGRIGG); Svolved in Immunocontraception.

Abstract

The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites. Anticancer peptides (ACPs) are polycationic amphiphiles capable of preferentially killing a widespectrum of cancer cells relative to non-cancerous cells. Their primary mode of action is aninteraction with the cell membrane and subsequent activation of lytic effects, however it remainscontroversial the exact mechanism responsible for this mode of action. It has in previous studies been shown that utilizing zeta potential analyses it was possible to demonstrate the interaction of a small anticancer peptide with membrane modelsystems and cancer cells. Electrostatic interactions have a pivotal role in the cell killing processand in contrast to the AMPs action cell death occurs without achieving full neutralization of themembrane charge. The advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Web-enabled service called GEMS (Gene Expression Mining Server) for biclustering microarray data where Users may upload expression data and specify a set of criteria.GEMS performs bicluster mining based on a Gibbs sampling paradigm. Here, we have for the first time discovered an In silico designed of an Anticancer Peptide SVS-1 multipharmacophore as a potential drug-like efficator in Preceding Membrane Neutralization using a Quantum coupled mutation finder for predicting functionally or structurally important sites and quantum Jensen-Shannon divergence CUDA programming multi-mimotopic algorithmic approach for biclustering analysis of expression data.

Abstract

Background

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 up to 1 microsecond on proteins, DNA, lipids and carbohydrates.

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 macromolecular structures and dynamics.

Keywords

molecular dynamics, empirical force field, potential energy function, molecular mechanics, computer-aided drug design, biophysics

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.Simulation of Quantum Dynamics Based on the Quantum Stochastic Differential EquationAn algorithm for high-resolution refinement and binding affinity estimation of inhibitors of CGQMCTVWCSSGC targeted conserved peptide substitution mimetic pharmacostructures antagonizing VEGFR-3-mediated oncogenic effects. Cancer is still a major cause of death in the world at the beginning of the- 21st century and remains a major focus for ongoing research and development. In recent years a promising approach to the therapeutic intervention of cancer has focused on antiangiogenesis therapies. VEGFR-3 was detected in advanced human malignancies and correlated with poor prognosis. Previous studies show that activation of the VEGF-C/VEGFR-3 axis promotes cancer metastasis and is associated with clinical progression in patients with lung cancer, indicating that VEGFR-3 is a potential target for cancer therapy. Initial screening has identified other promising VEGFR-3 binding peptides as well. For example, a peptide comprising any of the following amino acid sequences: SGYWWDTWF, SCYWRDTWF, KVGWSSPDW, FVGWTKVLG, YSSSMRWRH, RWRGNAYPG, SAVFRGRWL, WFSASLRFR, and conservative substitution-analogs thereof, binds human VEGFR-3. On the other hand a newly introduced binding energy funnel ‘steepness score’ was applied for the evaluation of the protein–peptide-multi-ligand complexes binding affinity. KNIME-based BiogenetoligandorolTM – Pepcrawler simulations predicted high binding affinity for native protein–peptide-hyper-ligand complexes benchmark and low affinity for low-energy decoy complexes. As a result we managed finally to introduce an algorithm Simulation of Quantum Dynamics Based on the Quantum Stochastic Differential Equation for high-resolution refinement and binding affinity estimation of inhibitors of CGQMCTVWCSSGC targeted conserved peptide substitution mimetic pharmacostructures antagonizing VEGFR-3-mediated oncogenic effects.

Keywords

RRT-based; algorithm;high-resolution; refinement;binding affinity; estimation;peptide inhibitors;In silico discovery; high resolution;docking; refinement;conserved peptide; substitution;mimetic; pharmacostructure;suppressor; VEGFR-3; Simulation of Quantum Dynamics; Quantum Stochastic; Differential Equation; algorithm for high-resolution refinement;

Abstract

Background

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.

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 macromolecular structures and dynamics.

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 CHARMM additive polarizable force fields for biophysics computer-aided drug design algorithms for high-resolution refinement and binding affinity estimation of inhibitors of CGQMCTVWCSSGC targeted conserved peptide substitution mimetic pharmacostructures antagonizing VEGFR-3-mediated oncogenic effects.

Keywords

molecular dynamics, empirical force field, potential energy function, molecular mechanics, computer-aided drug design, biophysics; CHARMM additive; polarizable force fields; biophysics; computer-aided drug design; algorithms; high-resolution refinement; binding affinity; estimation of inhibitors; CGQMCTVWCSSGC targeted; conserved peptide; substitution; mimetic pharmacostructures; VEGFR-3-mediated; oncogenic effects

Abstract

The main facts about the scale of time considered as a plot of a sequence of events are submitted both to a review and a more detailed calculation. Classical progressive character of the time variable, present in the everyday life and in the modern science, too, is compared with a circular-like kind of advancement of time. This second kind of the time behaviour can be found suitable when a perturbation process of a quantum-mechanical system is examined. In fact the paper demonstrates that the complicated high-order Schrödinger perturbation energy of a non-degenerate quantum state becomes easy to approach of the basis of Circular Scale of Time and Energy of a Quantum State Calculated from the Schrödinger Perturbation Theory for high-resolution refinement and binding affinity estimation of inhibitors of CGQMCTVWCSSGC targeted conserved peptide substitution mimetic pharmacostructures antagonizing VEGFR-3-mediated oncogenic effects. For example for the perturbation order N = 20 instead of 19! ≈ 1.216 × 1017 Feynman diagrams, the contribution of which should be derived and calculated, only less than 218 ≈ 2.621 × 105 terms belonging to N = 20 should be taken into account to the same purpose.

Keywords

Circular Scale of Time, Schrödinger Perturbation Theory, Non-Degenerate Quantum State;Circular Scale; Time and Energy; Quantum State; Schrödinger Perturbation; Theory for high-resolution refinement; binding affinity; inhibitors; CGQMCTVWCSSGC targeted; conserved peptide; substitution mimetic; pharmacostructures; antagonizing VEGFR-3;mediated oncogenic effects.

Abstract

Background

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 up to 1 microsecond on proteins, DNA, lipids and carbohydrates.

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 macromolecular structures and dynamics.

Keywords

algorithm estimation; CHARMM additive; polarizable force fields; biophysics; computer-aided drug design; high-resolution refinement; high binding affinity; inhibitors; CGQMCTVWCSSGC targeted; conserved peptide; substitution mimetic; pharmacostructures; VEGFR-3-mediated; oncogenic effects;

Abstract

Diabetes mellitus affects over 100 million individuals worldwide. In the U.S., the estimated healthcare costs of those affected by diabetes is approximately 136 billion dollars annually. Diabetes mellitus is a disorder of the metabolism that is characterized by the inability of the pancreas to secrete sufficient amounts of insulin, which results in large fluctuations in blood glucose levels and can have both short- and long-term physiological consequences. Glucagon-like peptide-1 (7-36) amide (GLP-1) is a gut hormone, released postprandially,which stimulates insulin secretion and insulin gene expression as well as pancreatic B-cell growth. Together with glucose-dependent insulinotropic polypeptide (GIP), it is responsible for the incretin effect which is the augmentation of insulin secretion following oral administration of glucose. We therefore for the first time provided in this scientific project a promising alternative to bridge the knowledge gap between insulinotropic biological conserved signaling pathways and chemistry informatic tools which significantly boost the productivity of our chemogenomics research for the distribution of quantum Fisher information in asymmetric cloning computational target fishing mining machinery as an identification tool for predicting therapeutic potential of GLP-1, INGAP-P and IGLHDPSHGTLPNGS peptide mimetic insulinotropic of high-potency compounds based on chemogenomic databases.

Keywords

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

Abstract

An unknown quantum state cannot be copied and broadcast freely due to the no-cloning theorem. Approximate cloning schemes have been proposed to achieve the optimal cloning characterized by the maximal fidelity between the original and its copies. Here, from the perspective of quantum Fisher information (QFI), we investigate the distribution of QFI in asymmetric cloning machines which produce two nonidentical copies. As one might expect, improving the QFI of one copy results in decreasing the QFI of the other copy. It is perhaps also unsurprising that asymmetric phase-covariant cloning outperforms universal cloning in distributing QFI since a priori information of the input state has been utilized. However, interesting results appear when we compare the distributabilities of fidelity (which quantifies the full information of quantum states), and QFI (which only captures the information of relevant parameters) in asymmetric cloning machines. Unlike the results of fidelity, where the distributability of symmetric cloning is always optimal for any d-dimensional cloning, we find that any asymmetric cloning outperforms symmetric cloning on the distribution of QFI for d ≤ 18, whereas some but not all asymmetric cloning strategies could be worse than symmetric ones when d > 18. Classical information can be replicated perfectly and broadcast without fundamental limitations. However, information encoded in quantum states is subject to several intrinsic restrictions of quantum mechanics, such as Heisenberg’s uncertainty relations1 and quantum no-cloning theorem2. The no-cloning theorem tells us that an unknown quantum state cannot be perfectly replicated because of the linearity of the time evolution in quantum physics, which is the essential prerequisite for the absolute security of quantum cryptography3. Nevertheless, it is still possible to clone a quantum state approximately, or instead, clone it perfectly with certain probability4,5 distributions of a computational target fishing mining quantum Fisher information in asymmetric cloning machinery as an Identification tool for predicting therapeutic potential of GLP-1, INGAP-P and IGLHDPSHGTLPNGS peptide mimetic insulinotropic of high-potency compounds based on chemogenomic databases.

Keywords

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

Abstract

The main facts about the scale of time considered as a plot of a sequence of events are submitted both to a review and a more detailed calculation. Classical progressive character of the time variable, present in the everyday life and in the modern science, too, is compared with a circular-like kind of advancement of time. This second kind of the time behaviour can be found suitable when a perturbation process of a quantum-mechanical system is examined. In fact the paper demonstrates that the complicated high-order Schrödinger perturbation energy of a non-degenerate quantum state becomes easy to approach of the basis of a circular scale. For example for the perturbation order N = 20 instead of 19! ≈ 1.216 × 1017 Feynman diagrams, the contribution of which should be derived and calculated, only less than 218 ≈ 2.621 × 105 terms belonging to N = 20 should be taken into account to the same purpose of Circular Scale of Time and Energy of a Quantum State Calculated from the Schrödinger Perturbation Theory as an identification tool for predicting therapeutic potential of GLP-1, INGAP-P and IGLHDPSHGTLPNGS peptide mimetic insulinotropic of high-potency compounds based on chemogenomic databases.

Keywords

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