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The oxygen-oxygen distance of water in crystallographic data sets.

ABSTRACT: Water is a key component of cellular biochemistry and numerous water molecules are visible in crystallographic structures. Here we report a series of data sets of crystallographic water: a high resolution data set, a cytochrome c oxidase (subunit I) data set and a carbonic anhydrase data set. These data support the evidence that short distance water molecule pairs are present both at the surface and inside the cavities of proteins. These data are related to article entitled "Oxygen-oxygen distances in protein-bound crystallographic water suggest the presence of protonated clusters" (Palese, 2020) [1].

SUBMITTER: Palese LL

PROVIDER: S-EPMC6965711 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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The oxygen-oxygen distance of water in crystallographic data sets.

Palese Luigi Leonardo LL

Data in brief 20200107

Water is a key component of cellular biochemistry and numerous water molecules are visible in crystallographic structures. Here we report a series of data sets of crystallographic water: a high resolution data set, a cytochrome <i>c</i> oxidase (subunit I) data set and a carbonic anhydrase data set. These data support the evidence that short distance water molecule pairs are present both at the surface and inside the cavities of proteins. These data are related to article entitled "Oxygen-oxygen ...[more]

PMID: 31970272

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Project description:BackgroundThe Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated.ResultsA fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. Finally, those clusters are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. An application for dengue fever in a small Brazilian city is presented.ConclusionsThe ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Furthermore, as VBScan also incorporates topological information from the point neighborhood structure, in addition to the usual geometric information, it is more robust than purely geometric methods such as the elliptic scan. Those advantages were illustrated in a real setting for dengue fever space-time clusters.

Dataset Information

The oxygen-oxygen distance of water in crystallographic data sets.

Publications

The oxygen-oxygen distance of water in crystallographic data sets.

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