A computational analysis of substrate binding strength by phosphorylase kinase and protein kinase A

Protein kinases exhibit various degrees of substrate specificity. The large number of different protein kinases in the eukaryotic proteomes makes it impractical to determine the specificity of each enzyme experimentally. To test if it were possible to discriminate potential substrates from non-substrates by simple computational techniques, we analysed the binding enthalpies of modelled enzyme-substrate complexes and attempted to correlate it with experimental enzyme kinetics measurements. The crystal structures of phosphorylase kinase and cAMP-dependent protein kinase were used to generate models of the enzyme with a series of known peptide substrates and non-substrates, and the approximate enthalpy of binding assessed following energy minimization. We show that the computed enthalpies do not correlate closely with kinetic measurements, but the method can distinguish good substrates from weak substrates and non-substrates. Copyright (C) 2002 John Wiley Sons, Ltd.

Prediction of promiscuous peptides that bind HLA class I molecules

Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, multipred, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. multipred is both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. multipred replaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that multipred can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.

Thermal Characteristics of Polybia scutellaris Nests (Hymenoptera: Vespidae) Using Computational Fluid Dynamics: A Possible Adaptation to Tropical Climates

Polybia scutellaris constructs huge nests characterized by numerous spinal projections on the surface. We investigated the thermal characteristics of P scutellaris nests in order to determine whether their nest temperature is homeothermically maintained and whether the spines play a role in the thermoregulation of the nests. In order to examine these hypotheses, we measured the nest temperature in a active nest and in an abandoned nest. The temperature in the active nest was almost stable at 27 degrees C, whereas that of the abandoned nest varied with changes in the ambient temperature, suggesting that nest temperature was maintained by the thermogenesis of colony individuals. In order to predict the thermal properties of the spines, a numerical simulation was employed. To construct a 3D-model of a P scutellaris nest, the nest architecture was simplified into an outer envelope and the surface spines, for both of which the initial temperature was set at 27 degrees C. The physical properties of the simulated nest were regarded to be those of wood since the nest of this species is constructed from plant materials. When the model was exposed to cool air (12 degrees C), the temperature was lower in the models with more spines. On the other hand, when the nest was heated (42 degrees C), the temperature increase was smaller in models with more spines. It is suggested that the spines act as a heat radiator, not as an insulator, against the changes in ambient temperature.

Generation of Naphthoquinone Radical Anions by Electrospray Ionization: Solution, Gas-Phase, and Computational Chemistry Studies

Radical anions are present in several chemical processes, and understanding the reactivity of these species may be described by their thermodynamic properties. Over the last years, the formation of radical ions in the gas phase has been an important issue concerning electrospray ionization mass spectrometry studies. In this work, we report on the generation of radical anions of quinonoid compounds (Q) by electrospray ionization mass spectrometry. The balance between radical anion formation and the deprotonated molecule is also analyzed by influence of the experimental parameters (gas-phase acidity, electron affinity, and reduction potential) and solvent system employed. The gas-phase parameters for formation of radical species and deprotonated species were achieved on the basis of computational thermochemistry. The solution effects on the formation of radical anion (Q(center dot-)) and dianion (Q(2-)) were evaluated on the basis of cyclic voltammetry analysis and the reduction potentials compared with calculated electron affinities. The occurrence of unexpected ions [Q + 15](-) was described as being a reaction between the solvent system and the radical anion, Q(center dot-).The gas-phase chemistry of the electrosprayed radical anions was obtained by collisional-induced dissociation and compared to the relative energy calculations. These results are important for understanding the formation and reactivity of radical anions and to establish their correlation with the reducing properties by electrospray ionization analyses.

A Computational Study of Tetrafluoro-[2.2]Cyclophanes

A computational study of the isomers of tetrafluorinated [2.2]cyclophanes persubstituted in one ring, namely F-4-[2.2]paracyclophane (4), F-4-anti-[2.2]metacyclophane (5a), F-4-syn-[2.2]metacyclophane (5b), and F-4-[2.2]metaparacyclophane (6a and 6b), was carried out. The effects of fluorination on the geometries, relative energies, local and global aromaticity, and strain energies of the bridges and rings were investigated. An analysis of the electron density by B3PW91/6-31+G(d,p), B3LYP/6-31+G(d,p), and MP2/6-31+G(d,p) was carried out using the natural bond orbitals (NBO), natural steric analysis (NSA), and atoms in molecules (AIM) methods. The analysis of frontier molecular orbitals (MOs) was also employed. The results indicated that the molecular structure of [2.2]paracyclophane is the most affected by the fluorination. Isodesmic reactions showed that the fluorinated rings are more strained than the nonfluorinated ones. The NICS, HOMA, and PDI criteria evidenced that the fluorination affects the aromaticity of both the fluorinated and the nonfluorinated rings. The NBO and NSA analyses gave an indication that the fluorination increases not only the number of through-space interactions but also their magnitude. The AIM analysis suggested that the through-space interactions are restricted to the F-4-[2.2]metacyclophanes. In addition, the atomic properties, computed over the atomic basins, shave evidence that not only the substitution, but also the position of the bridges could affect the atomic charges. the first atomic moments, and the atomic volumes.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

The immunology of Epstein-Barr virus infection

Epstein-Barr virus is a classic example of a persistent human virus that has caught the imagination of immunologists, virologists and oncologists because of the juxtaposition of a number of important properties. First, the ability of the virus to immortalize B lymphocytes in vitro has provided an antigen presenting cell in which all the latent antigens: of the virus are displayed and are available for systematic study. Second, the virus presents an ideal system for studying the immune parameters that maintain latency and the consequences of disturbing this cell-virus relationship. Third, this wealth of immunological background has provided a platform for elucidating the role of the immune system in protection from viral-associated malignancies of B cell and epithelial cell origin. Finally attention is now being directed towards the development of vaccine formulations which might have broad application in the control of human malignancies.

Branching processes and computational collapse of discretized unimodal mappings

In computer simulations of smooth dynamical systems, the original phase space is replaced by machine arithmetic, which is a finite set. The resulting spatially discretized dynamical systems do not inherit all functional properties of the original systems, such as surjectivity and existence of absolutely continuous invariant measures. This can lead to computational collapse to fixed points or short cycles. The paper studies loss of such properties in spatial discretizations of dynamical systems induced by unimodal mappings of the unit interval. The problem reduces to studying set-valued negative semitrajectories of the discretized system. As the grid is refined, the asymptotic behavior of the cardinality structure of the semitrajectories follows probabilistic laws corresponding to a branching process. The transition probabilities of this process are explicitly calculated. These results are illustrated by the example of the discretized logistic mapping.

Computational differentiation of N-terminal signal peptides and transmembrane helices

Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies. (C) 2003 Elsevier Inc. All rights reserved.

Computational evaluation of hydraulic system behaviour with entrapped air under rapid pressurization

The pressurization of hydraulic systems containing entrapped air is considered a critical condition for the infrastructure's security due to transient pressure variations often occurred. The objective of the present study is the computational evaluation of trends observed in variation of maximum surge pressure resulting from rapid pressurizations. The comparison of the results with those obtained in previous studies is also undertaken. A brief state of art in this domain is presented. This research work is applied to an experimental system having entrapped air in the top of a vertical pipe section. The evaluation is developed through the elastic model based on the method of characteristics, considering a moving liquid boundary, with the results being compared with those achieved with the rigid liquid column model.

Computational evaluation of hydraulic system behaviour with entrapped air under rapid pressurization

The pressurization of hydraulic systems containing entrapped air is considered a critical condition for the infrastructure's security due to transient pressure variations often occurred. The objective of the present study is the computational evaluation of trends observed in variation of maximum surge pressure resulting from rapid pressurizations. The comparison of the results with those obtained in previous studies is also undertaken. A brief state of art in this domain is presented. This research work is applied to an experimental system having entrapped air in the top of a vertical pipe section. The evaluation is developed through the elastic model based on the method of characteristics, considering a moving liquid boundary, with the results being compared with those achieved with the rigid liquid column model.

Computational intelligence applications for future power systems

Power system planning, control and operation require an adequate use of existing resources as to increase system efficiency. The use of optimal solutions in power systems allows huge savings stressing the need of adequate optimization and control methods. These must be able to solve the envisaged optimization problems in time scales compatible with operational requirements. Power systems are complex, uncertain and changing environments that make the use of traditional optimization methodologies impracticable in most real situations. Computational intelligence methods present good characteristics to address this kind of problems and have already proved to be efficient for very diverse power system optimization problems. Evolutionary computation, fuzzy systems, swarm intelligence, artificial immune systems, neural networks, and hybrid approaches are presently seen as the most adequate methodologies to address several planning, control and operation problems in power systems. Future power systems, with intensive use of distributed generation and electricity market liberalization increase power systems complexity and bring huge challenges to the forefront of the power industry. Decentralized intelligence and decision making requires more effective optimization and control techniques techniques so that the involved players can make the most adequate use of existing resources in the new context. The application of computational intelligence methods to deal with several problems of future power systems is presented in this chapter. Four different applications are presented to illustrate the promises of computational intelligence, and illustrate their potentials.

Computational analysis of quinoxalines N,N-Dioxide and ITS Derivates

A quinoxalina e seus derivativos são uma importante classe de compostos heterocíclicos, onde os elementos N, S e O substituem átomos de carbono no anel. A fórmula molecular da quinoxalina é C8H6N2, formada por dois anéis aromáticos, benzeno e pirazina. É rara em estado natural, mas a sua síntese é de fácil execução. Modificações na estrutura da quinoxalina proporcionam uma grande variedade de compostos e actividades, tais como actividades antimicrobiana, antiparasitária, antidiabética, antiproliferativa, anti-inflamatória, anticancerígena, antiglaucoma, antidepressiva apresentando antagonismo do receptor AMPA. Estes compostos também são importantes no campo industrial devido, por exemplo, ao seu poder na inibição da corrosão do metal. A química computacional, ramo natural da química teórica é um método bem desenvolvido, utilizado para representar estruturas moleculares, simulando o seu comportamento com as equações da física quântica e clássica. Existe no mercado uma grande variedade de ferramentas informaticas utilizadas na química computacional, que permitem o cálculo de energias, geometrias, frequências vibracionais, estados de transição, vias de reação, estados excitados e uma variedade de propriedades baseadas em várias funções de onda não correlacionadas e correlacionadas. Nesta medida, a sua aplicação ao estudo das quinoxalinas é importante para a determinação das suas características químicas, permitindo uma análise mais completa, em menos tempo, e com menos custos.