Glycans are key molecules in many physiological and pathological processes. As with other molecules, like proteins, visualization of the 3D structures of glycans adds valuable information for understanding their biological function. Hence, here we introduce Azahar, a computing environment for the creation, visualization and analysis of glycan molecules. Azahar is implemented in Python and works as a plugin for the well known PyMOL package (Schrodinger in The PyMOL molecular graphics system, version 1.3r1, 2010). Besides the already available visualization and analysis options provided by PyMOL, Azahar includes 3 cartoon-like representations and tools for 3D structure caracterization such as a comformational search using a Monte Carlo with minimization routine and also tools to analyse single glycans or trajectories/ensembles including the calculation of radius of gyration, Ramachandran plots and hydrogen bonds. Azahar is freely available to download from http://www.pymolwiki.org/index.php/Azahar and the source code is available at https://github.com/agustinaarroyuelo/Azahar .
There is a need to develop widely applicable tools to understand glycan organization, diversity and structure. We present a graph-theoretical study of a large sample of glycans in terms of finite dimension, a new metric which is an adaptation to finite sets of the classical Hausdorff “fractal” dimension. Every glycan in the sample is encoded, via finite dimension, as a point of Glycan Space, a new notion introduced in this paper. Two major outcomes were found: (a) the existence of universal bounds that restrict the universe of possible glycans and show, for instance, that the graphs of glycans are a very special type of chemical graph, and (b) how Glycan Space is related to biological domains associated to the analysed glycans. In addition, we discuss briefly how this encoding may help to improve search in glycan databases.
In the present work we explore three different approaches for the computation of the one-bond spin-spin coupling constants (SSCC) 1 J CαH in proteins: DFT calculation, Karplus-like equation and Gaussian Process regression. The main motivation of this work is to select the best method for a fast and accurate computation of the 1 J CαH SSCC, for its use on everyday applications in protein structure validation, refinement and/or determination. Our initial results showed a poor agreement between the DFT computed and observed 1 J CαH SSCC values. Further analysis lead us to the understanding that the model chosen for the DFT computations is inappropriate, and that more complex models will requiere a higher if not prohibitively computational cost. Finally, we show that Karplus-like equation and Gaussian Process regression provide faster and more accurate results than DFT-based calculations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.