Computations to Obtain Wider Tunnels in Protein Structures

Finding wide tunnels in protein structures is an important problem in Structural Bioinformatics with applications in various areas such as drug design. Several algorithms have been proposed for finding wide tunnels in a fixed protein conformation. However, to the best of our knowledge, none of the existing work have considered widening the tunnel, i.e., finding a wider tunnel in an alternative conformation of the given structure. In this thesis we initiate this line of research by proposing a tunnel-widening algorithm which aims to make the tunnel wider by a slight local change in the structure of the protein. Given a fixed conformation of a protein with a point located inside it, we first describe an algorithm to identify the widest tunnel from that point to the outside environment of the protein. Then we try to make the tunnel wider by considering various alternative conformations of the protein. We only consider conformations whose energies are not much higher than the energy of the initial conformation. Among these alternative conformations we select the one with the widest tunnel. However, the alternative conformation with the widest tunnel might not be accessible from the initial structure. Thus, in the next step we develop three algorithms for finding a feasible transition pathway from the initial structure to the alternative conformation, i.e., a sequence of intermediate conformations between the initial structure and the alternative conformation such that the energy values of all these intermediate conformations are close to the energy of the initial structure. We evaluate our tunnel-finding and tunnel-widening algorithms on various proteins. Our experiments show that in most cases we can make the tunnel wider in an alternative conformation. However, there are cases in which we find a wider tunnel in an alternative conformation, but the energy value of the alternative conformation is much higher than the energy of the initial structure. We also implemented our three pathway-finding algorithms and tested them on various instances. Our experiments show that although in most cases we can find a feasible transition pathway, there are cases in which the alternative conformation has energy close to the initial structure, but our algorithms cannot find any feasible pathway from the initial structure to the alternative conformation. Furthermore, there is a trade-off between the running time and accuracy of the three pathway-finding algorithms

GenGIS 2: geospatial analysis of traditional and genetic biodiversity, with new gradient algorithms and an extensible plugin framework

GenGIS is free and open source software designed to integrate biodiversity data with a digital map and information about geography and habitat. While originally developed with microbial community analyses and phylogeography in mind, GenGIS has been applied to a wide range of datasets. A key feature of GenGIS is the ability to test geographic axes that can correspond to routes of migration or gradients that influence community similarity. Here we introduce GenGIS version 2, which extends the linear gradient tests introduced in the first version to allow comprehensive testing of all possible linear geographic axes. GenGIS v2 also includes a new plugin framework that supports the development and use of graphically driven analysis packages: initial plugins include implementations of linear regression and the Mantel test, calculations of alpha-diversity (e.g., Shannon Index) for all samples, and geographic visualizations of dissimilarity matrices. We have also implemented a recently published method for biomonitoring reference condition analysis (RCA), which compares observed species richness and diversity to predicted values to determine whether a given site has been impacted. The newest version of GenGIS supports vector data in addition to raster files. We demonstrate the new features of GenGIS by performing a full gradient analysis of an Australian kangaroo apple data set, by using plugins and embedded statistical commands to analyze human microbiome sample data, and by applying RCA to a set of samples from Atlantic Canada. GenGIS release versions, tutorials and documentation are freely available at http://kiwi.cs.dal.ca/GenGIS, and source code is available at https://github.com/beiko-lab/gengis

Heatmap of frequencies of three taxonomic groups (Bacteroidia, Clostridia, and “Unclassified Bacteria”) from 24 fecal samples.

<p>Dark colors correspond to low frequencies, while yellow, tan and pink indicate high frequencies. Hierarchical clustering of samples and taxonomic groups are shown along both dimensions of the heatmap. Sample labels are explained in the legend of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069885#pone-0069885-g002" target="_blank">Figure 2b</a>.</p

Phylogeography of kangaroo apples.

<p>A) A longitudinal gradient resulting in 23 crossings. Each of the eight species within the kangaroo apple phylogeny is assigned a unique color, and the two most substantial subclades are labelled. B) A latitudinal gradient results in 57 crossings. C) Results of a linear axes analysis on the kangaroo apple dataset. The number of crossings is only shown for axes between 90° and 270° as the graph has a period of 180°. Under the null model, only 10 of 10,000 permutations resulted in fewer than 34 crossings which is depicted by the red line (i.e. α = 0.001). D) A linear axes analysis of the <i>Similia</i> subclade with the red line set to reflect a conservative critical value of α = 0.1. E) A linear axes analysis of the <i>Avicularia/Laciniata</i> subclades (α = 0.1).</p