University of Missouri

About: University of Missouri is a education organization based out in Columbia, Missouri, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 41427 authors who have published 83598 publications receiving 2911437 citations. The organization is also known as: Mizzou & Missouri-Columbia.

Dystrophins carrying spectrin-like repeats 16 and 17 anchor nNOS to the sarcolemma and enhance exercise performance in a mouse model of muscular dystrophy

Abstract: Sarcolemma-associated neuronal NOS (nNOS) plays a critical role in normal muscle physiology. In Duchenne muscular dystrophy (DMD), the loss of sarcolemmal nNOS leads to functional ischemia and muscle damage; however, the mechanism of nNOS subcellular localization remains incompletely understood. According to the prevailing model, nNOS is recruited to the sarcolemma by syntrophin, and in DMD this localization is altered. Intriguingly, the presence of syntrophin on the membrane does not always restore sarcolemmal nNOS. Thus, we wished to determine whether dystrophin functions in subcellular localization of nNOS and which regions may be necessary. Using in vivo transfection of dystrophin deletion constructs, we show that sarcolemmal targeting of nNOS was dependent on the spectrin-like repeats 16 and 17 (R16/17) within the rod domain. Treatment of mdx mice (a DMD model) with R16/17-containing synthetic dystrophin genes effectively ameliorated histological muscle pathology and improved muscle strength as well as exercise performance. Furthermore, sarcolemma-targeted nNOS attenuated α-adrenergic vasoconstriction in contracting muscle and improved muscle perfusion during exercise as measured by Doppler and microsphere circulation. In summary, we have identified the dystrophin spectrin-like repeats 16 and 17 as a novel scaffold for nNOS sarcolemmal targeting. These data suggest that muscular dystrophy gene therapies based on R16/17-containing dystrophins may yield better clinical outcomes than the current therapies.

Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds

Abstract: Spatiotemporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal variability. The complexity of these processes and the large number of observation/prediction locations preclude the use of traditional covariance-based spatiotemporal statistical methods. Alternatively, we focus on conditionally specified (i.e., hierarchical) spatiotemporal models. These methods offer several advantages over traditional approaches. Primarily, physical and dynamical constraints can be easily incorporated into the conditional formulation, so that the series of relatively simple yet physically realistic conditional models leads to a much more complicated spatiotemporal covariance structure than can be specified directly. Furthermore, by making use of the sparse structure inherent in the hierarchical approach, as well as multiresolution (wavelet) bases, the models can be computed ...

Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking

Abstract: One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.