3 resultados para algebraic structures of integrable models

em DigitalCommons@The Texas Medical Center


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Grass carp reovirus (GCRV) is a member of the Aquareovirus genus of the family Reoviridae, a large family of double-stranded RNA (dsRNA) viruses infecting plants, insects, fishes and mammals. We report the first subnanometer-resolution three-dimensional structures of both GCRV core and virion by cryoelectron microscopy. These structures have allowed the delineation of interactions among the over 1000 molecules in this enormous macromolecular machine and a detailed comparison with other dsRNA viruses at the secondary-structure level. The GCRV core structure shows that the inner proteins have strong structural similarities with those of orthoreoviruses even at the level of secondary-structure elements, indicating that the structures involved in viral dsRNA interaction and transcription are highly conserved. In contrast, the level of similarity in structures decreases in the proteins situated in the outer layers of the virion. The proteins involved in host recognition and attachment exhibit the least similarities to other members of Reoviridae. Furthermore, in GCRV, the RNA-translocating turrets are in an open state and lack a counterpart for the sigma1 protein situated on top of the close turrets observed in mammalian orthoreovirus. Interestingly, the distribution and the organization of GCRV core proteins resemble those of the cytoplasmic polyhedrosis virus, a cypovirus and the structurally simplest member of the Reoviridae family. Our results suggest that GCRV occupies a unique structure niche between the simpler cypoviruses and the considerably more complex mammalian orthoreovirus, thus providing an important model for understanding the structural and functional conservation and diversity of this enormous family of dsRNA viruses.

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Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

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Na(+)/Ca(2+) exchangers (NCX) constitute a major Ca(2+) export system that facilitates the re-establishment of cytosolic Ca(2+) levels in many tissues. Ca(2+) interactions at its Ca(2+) binding domains (CBD1 and CBD2) are essential for the allosteric regulation of Na(+)/Ca(2+) exchange activity. The structure of the Ca(2+)-bound form of CBD1, the primary Ca(2+) sensor from canine NCX1, but not the Ca(2+)-free form, has been reported, although the molecular mechanism of Ca(2+) regulation remains unclear. Here, we report crystal structures for three distinct Ca(2+) binding states of CBD1 from CALX, a Na(+)/Ca(2+) exchanger found in Drosophila sensory neurons. The fully Ca(2+)-bound CALX-CBD1 structure shows that four Ca(2+) atoms bind at identical Ca(2+) binding sites as those found in NCX1 and that the partial Ca(2+) occupancy and apoform structures exhibit progressive conformational transitions, indicating incremental regulation of CALX exchange by successive Ca(2+) binding at CBD1. The structures also predict that the primary Ca(2+) pair plays the main role in triggering functional conformational changes. Confirming this prediction, mutagenesis of Glu(455), which coordinates the primary Ca(2+) pair, produces dramatic reductions of the regulatory Ca(2+) affinity for exchange current, whereas mutagenesis of Glu(520), which coordinates the secondary Ca(2+) pair, has much smaller effects. Furthermore, our structures indicate that Ca(2+) binding only enhances the stability of the Ca(2+) binding site of CBD1 near the hinge region while the overall structure of CBD1 remains largely unaffected, implying that the Ca(2+) regulatory function of CBD1, and possibly that for the entire NCX family, is mediated through domain interactions between CBD1 and the adjacent CBD2 at this hinge.