949 resultados para Inverse computational method


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The search for novel leads is a critical step in the drug discovery process. Computational approaches to identify new lead molecules have focused on discovering complete ligands by evaluating the binding affinity of a large number of candidates, a task of considerable complexity. A new computational method is introduced in this work based on the premise that the primary molecular recognition event in the protein binding site may be accomplished by small core fragments that serve as molecular anchors, providing a structurally stable platform that can be subsequently tailored into complete ligands. To fulfill its role, we show that an effective molecular anchor must meet both the thermodynamic requirement of relative energetic stability of a single binding mode and its consistent kinetic accessibility, which may be measured by the structural consensus of multiple docking simulations. From a large number of candidates, this technique is able to identify known core fragments responsible for primary recognition by the FK506 binding protein (FKBP-12), along with a diverse repertoire of novel molecular cores. By contrast, absolute energetic criteria for selecting molecular anchors are found to be promiscuous. A relationship between a minimum frustration principle of binding energy landscapes and receptor-specific molecular anchors in their role as "recognition nuclei" is established, thereby unraveling a mechanism of lead discovery and providing a practical route to receptor-biased computational combinatorial chemistry.

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Fragments of proteins (short peptides) that "fold" suggest a mechanism of how complete conformational search in protein folding is avoided. We used a computational method to determine structures of two foldable peptides in explicit water: RVEW and CSVTC. The optimization starts from random structures and no experimental constraints are used. In agreement with NMR data, the simulations find a hydrophobic pair (Val/Trp) in REVW. The structure of CSVTC is induced by a surface water that bridges two amide hydrogens, a drive to structure hypothesized by Ben-Naim [Ben-Naim, A. (1990) J. Chem. Phys. 93, 8196-8210] that is largely ignored in studies of folding. Tendency to structure in short peptide chains suggests a mechanism for the formation of short-range nucleation sites in protein folding.

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Neste projeto foi desenvolvido um método computacional para verificação da melhor combinação tela intensificadora - filme para exames mamográficos através do estudo de suas características sensitométricas. O software, desenvolvido em ambiente Delphi para windows, apresenta na tela do microcomputador a imagem a ser obtida para cada tipo de combinação tela intensificadora - filme, utilizando imagens de \"Phantoms\" e de mamas reais. Em razão da ampla quantidade de fatores que influenciam a imagem mamográfica final, tais como magnificação, característica dos filmes e telas intensificadoras e condições da processadora, o método proposto pode proporcionar uma ampla avaliação da qualidade dos sistemas de imagem mamográfica de uma forma simples, rápida e automática, através de procedimentos de simulação computacional. A simulação investigou a influência que um determinado sistema de registro exerce sobre a qualidade da imagem, possibilitando conhecer previamente a imagem final a ser obtida com diferentes equipamentos e sistemas de registro. Dentre os sistemas investigados, três filmes (Kodak Min R 2000, Fuji UM MA-HC e Fuji ADM) e duas telas intensificadoras (Kodak Min R 2000 e Fuji AD Mammo Fine), aquele que apresentou melhores resultados, com melhor qualidade de imagens e menor exposição à paciente foi o de tela Min R 2000 com filme Min R 2000 da Kodak.

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High-quality data about protein structures and their gene sequences are essential to the understanding of the relationship between protein folding and protein coding sequences. Firstly we constructed the EcoPDB database, which is a high-quality database of Escherichia coli genes and their corresponding PDB structures. Based on EcoPDB, we presented a novel approach based on information theory to investigate the correlation between cysteine synonymous codon usages and local amino acids flanking cysteines, the correlation between cysteine synonymous codon usages and synonymous codon usages of local amino acids flanking cysteines, as well as the correlation between cysteine synonymous codon usages and the disulfide bonding states of cysteines in the E. coli genome. The results indicate that the nearest neighboring residues and their synonymous codons of the C-terminus have the greatest influence on the usages of the synonymous codons of cysteines and the usage of the synonymous codons has a specific correlation with the disulfide bond formation of cysteines in proteins. The correlations may result from the regulation mechanism of protein structures at gene sequence level and reflect the biological function restriction that cysteines pair to form disulfide bonds. The results may also be helpful in identifying residues that are important for synonymous codon selection of cysteines to introduce disulfide bridges in protein engineering and molecular biology. The approach presented in this paper can also be utilized as a complementary computational method and be applicable to analyse the synonymous codon usages in other model organisms. (c) 2005 Elsevier Ltd. All rights reserved.

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Motivation: Conformational flexibility is essential to the function of many proteins, e.g. catalytic activity. To assist efforts in determining and exploring the functional properties of a protein, it is desirable to automatically identify regions that are prone to undergo conformational changes. It was recently shown that a probabilistic predictor of continuum secondary structure is more accurate than categorical predictors for structurally ambivalent sequence regions, suggesting that such models are suited to characterize protein flexibility. Results: We develop a computational method for identifying regions that are prone to conformational change directly from the amino acid sequence. The method uses the entropy of the probabilistic output of an 8-class continuum secondary structure predictor. Results for 171 unique amino acid sequences with well-characterized variable structure (identified in the 'Macromolecular movements database') indicate that the method is highly sensitive at identifying flexible protein regions, but false positives remain a problem. The method can be used to explore conformational flexibility of proteins (including hypothetical or synthetic ones) whose structure is yet to be determined experimentally.

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We introduce a general Hamiltonian describing coherent superpositions of Cooper pairs and condensed molecular bosons. For particular choices of the coupling parameters, the model is integrable. One integrable manifold, as well as the Bethe ansatz solution, was found by Dukelsky et al. [J. Dukelsky, G.G. Dussel, C. Esebbag, S. Pittel, Phys. Rev. Lett. 93 (2004) 050403]. Here we show that there is a second integrable manifold, established using the boundary quantum inverse scattering method. In this manner we obtain the exact solution by means of the algebraic Bethe ansatz. In the case where the Cooper pair energies are degenerate we examine the relationship between the spectrum of these integrable Hamiltonians and the quasi-exactly solvable spectrum of particular Schrodinger operators. For the solution we derive here the potential of the Schrodinger operator is given in terms of hyperbolic functions. For the solution derived by Dukelsky et al., loc. cit. the potential is sextic and the wavefunctions obey PT-symmetric boundary conditions. This latter case provides a novel example of an integrable Hermitian Hamiltonian acting on a Fock space whose states map into a Hilbert space of PE-symmetric wavefunctions defined on a contour in the complex plane. (c) 2006 Elsevier B.V. All rights reserved.

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We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.

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We analyze the stochastic creation of a single bound state (BS) in a random potential with a compact support. We study both the Hermitian Schrödinger equation and non-Hermitian Zakharov-Shabat systems. These problems are of special interest in the inverse scattering method for Korteveg–de-Vries and the nonlinear Schrödinger equations since soliton solutions of these two equations correspond to the BSs of the two aforementioned linear eigenvalue problems. Analytical expressions for the average width of the potential required for the creation of the first BS are given in the approximation of delta-correlated Gaussian potential and additionally different scenarios of eigenvalue creation are discussed for the non-Hermitian case.

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This thesis is about the study of relationships between experimental dynamical systems. The basic approach is to fit radial basis function maps between time delay embeddings of manifolds. We have shown that under certain conditions these maps are generically diffeomorphisms, and can be analysed to determine whether or not the manifolds in question are diffeomorphically related to each other. If not, a study of the distribution of errors may provide information about the lack of equivalence between the two. The method has applications wherever two or more sensors are used to measure a single system, or where a single sensor can respond on more than one time scale: their respective time series can be tested to determine whether or not they are coupled, and to what degree. One application which we have explored is the determination of a minimum embedding dimension for dynamical system reconstruction. In this special case the diffeomorphism in question is closely related to the predictor for the time series itself. Linear transformations of delay embedded manifolds can also be shown to have nonlinear inverses under the right conditions, and we have used radial basis functions to approximate these inverse maps in a variety of contexts. This method is particularly useful when the linear transformation corresponds to the delay embedding of a finite impulse response filtered time series. One application of fitting an inverse to this linear map is the detection of periodic orbits in chaotic attractors, using suitably tuned filters. This method has also been used to separate signals with known bandwidths from deterministic noise, by tuning a filter to stop the signal and then recovering the chaos with the nonlinear inverse. The method may have applications to the cancellation of noise generated by mechanical or electrical systems. In the course of this research a sophisticated piece of software has been developed. The program allows the construction of a hierarchy of delay embeddings from scalar and multi-valued time series. The embedded objects can be analysed graphically, and radial basis function maps can be fitted between them asynchronously, in parallel, on a multi-processor machine. In addition to a graphical user interface, the program can be driven by a batch mode command language, incorporating the concept of parallel and sequential instruction groups and enabling complex sequences of experiments to be performed in parallel in a resource-efficient manner.

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The thesis aims to define further the biometric correlates in anisometropic eyes in order to provide a structural foundation for propositions concerning the development of ametropia.Biometric data are presented for 40 anisometropes and 40 isometropic controls drawn from Caucasian and Chinese populations.The principal finding was that the main structural correlate of myopia is an increase in axial rather than equatorial dimensions of the posterior globe. This finding has not been previously reported for in vivo work on humans. The computational method described in the thesis is a more accessible method for determination of eye shape than current imaging techniques such as magnetic resonance imaging or laser Doppler interferometry (LDI). Retinal contours derived from LDI and computation were shown to be closely matched. Corneal topography revealed no differences in corneal characteristics in anisometropic eyes, which supports the finding that anisometropia arises from differences in vitreous chamber depth.The corollary to axial expansion in myopia, that is retinal stretch in central regions of the posterior pole, was investigated by measurement of disc-to-fovea distances (DFD) using a scanning laser ophthalmoscope. DFD was found to increase with increased myopia, which demonstrates the primary contribution made by posterior central regions of the globe to axial expansion.The ocular pulse volume and choroidal blood flow, measured with the Ocular Blood Flow Tonograph, were found to be reduced in myopia; the reductions were found to be significantly correlated with vitreous chamber depth. The thesis includes preliminary data on whether the relationship arises from the influx of a blood bolus into eyes of different posterior volumes or represents actual differences in choroidal blood flow.The results presented in this thesis show the utility of computed retinal contour and demonstrate that the structural correlate of myopia is axial rather than equatorial expansion of the vitreous chamber. The technique is suitable for large population studies and its relative simplicity makes it feasible for longitudinal studies on the development of ametropia in, for example, children.

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Background The HIV virus is known for its ability to exploit numerous genetic and evolutionary mechanisms to ensure its proliferation, among them, high replication, mutation and recombination rates. Sliding MinPD, a recently introduced computational method [1], was used to investigate the patterns of evolution of serially-sampled HIV-1 sequence data from eight patients with a special focus on the emergence of X4 strains. Unlike other phylogenetic methods, Sliding MinPD combines distance-based inference with a nonparametric bootstrap procedure and automated recombination detection to reconstruct the evolutionary history of longitudinal sequence data. We present serial evolutionary networks as a longitudinal representation of the mutational pathways of a viral population in a within-host environment. The longitudinal representation of the evolutionary networks was complemented with charts of clinical markers to facilitate correlation analysis between pertinent clinical information and the evolutionary relationships. Results Analysis based on the predicted networks suggests the following:: significantly stronger recombination signals (p = 0.003) for the inferred ancestors of the X4 strains, recombination events between different lineages and recombination events between putative reservoir virus and those from a later population, an early star-like topology observed for four of the patients who died of AIDS. A significantly higher number of recombinants were predicted at sampling points that corresponded to peaks in the viral load levels (p = 0.0042). Conclusion Our results indicate that serial evolutionary networks of HIV sequences enable systematic statistical analysis of the implicit relations embedded in the topology of the structure and can greatly facilitate identification of patterns of evolution that can lead to specific hypotheses and new insights. The conclusions of applying our method to empirical HIV data support the conventional wisdom of the new generation HIV treatments, that in order to keep the virus in check, viral loads need to be suppressed to almost undetectable levels.

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In this paper, we will address together the magnetic and electrical properties of a particular semiconductor, the GaMnAs. The treatment will be done analytically in the first part of the work, according to the computational method for simulation of physical systems through the implementation of the expressions obtained in the first part. All study of magnetic contribution will be made using an interaction Kondo type, using an approach by Green functions. The electrical part, which consists of the Coulomb interactions between carriers and Mn ions, will be treated within the approach of multiple scattering. The implementation of the proposed method will calculate the Green functions converged as multiple scattering solution and use them as a starting point for the calculation of the effective magnetic interactions between Mn ions mediated charge carriers. The concentration parameters were varied for Mn ions and carriers as well. The combination of these two parameters can lead to insulating, metal samples with carriers in Fermi level to low or high mobility. As a result a correlation between the obtained carrier mobility and the strength of magnetic interaction. The greater mobility, the greater the intensity of the interaction.

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Testing for differences within data sets is an important issue across various applications. Our work is primarily motivated by the analysis of microbiomial composition, which has been increasingly relevant and important with the rise of DNA sequencing. We first review classical frequentist tests that are commonly used in tackling such problems. We then propose a Bayesian Dirichlet-multinomial framework for modeling the metagenomic data and for testing underlying differences between the samples. A parametric Dirichlet-multinomial model uses an intuitive hierarchical structure that allows for flexibility in characterizing both the within-group variation and the cross-group difference and provides very interpretable parameters. A computational method for evaluating the marginal likelihoods under the null and alternative hypotheses is also given. Through simulations, we show that our Bayesian model performs competitively against frequentist counterparts. We illustrate the method through analyzing metagenomic applications using the Human Microbiome Project data.

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Free energy calculations are a computational method for determining thermodynamic quantities, such as free energies of binding, via simulation.

Currently, due to computational and algorithmic limitations, free energy calculations are limited in scope.

In this work, we propose two methods for improving the efficiency of free energy calculations.

First, we expand the state space of alchemical intermediates, and show that this expansion enables us to calculate free energies along lower variance paths.

We use Q-learning, a reinforcement learning technique, to discover and optimize paths at low computational cost.

Second, we reduce the cost of sampling along a given path by using sequential Monte Carlo samplers.

We develop a new free energy estimator, pCrooks (pairwise Crooks), a variant on the Crooks fluctuation theorem (CFT), which enables decomposition of the variance of the free energy estimate for discrete paths, while retaining beneficial characteristics of CFT.

Combining these two advancements, we show that for some test models, optimal expanded-space paths have a nearly 80% reduction in variance relative to the standard path.

Additionally, our free energy estimator converges at a more consistent rate and on average 1.8 times faster when we enable path searching, even when the cost of path discovery and refinement is considered.

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Les protéines membranaires intégrales jouent un rôle indispensable dans la survie des cellules et 20 à 30% des cadres de lectures ouverts codent pour cette classe de protéines. La majorité des protéines membranaires se trouvant sur la Protein Data Bank n’ont pas une orientation et une insertion connue. L’orientation, l’insertion et la conformation que les protéines membranaires ont lorsqu’elles interagissent avec une bicouche lipidique sont importantes pour la compréhension de leur fonction, mais ce sont des caractéristiques difficiles à obtenir par des méthodes expérimentales. Des méthodes computationnelles peuvent réduire le temps et le coût de l’identification des caractéristiques des protéines membranaires. Dans le cadre de ce projet de maîtrise, nous proposons une nouvelle méthode computationnelle qui prédit l’orientation et l’insertion d’une protéine dans une membrane. La méthode est basée sur les potentiels de force moyenne de l’insertion membranaire des chaînes latérales des acides aminés dans une membrane modèle composèe de dioléoylphosphatidylcholine.