Automated building of carbohydrate molecules using X-ray crystallography data

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Carbohydrate molecules are an essential part of the living world, making up the sugars in our food, the fibres in clothes and the cell walls of green plants. The interaction of carbohydrates with other biological molecules, and in particular proteins, is an important part of many biological processes. Understanding this interaction is important for understanding how cells work and interact with one another, as well as being important to diverse bio-technologies such as the breaking down of fibrous landfill waste and the development of biological washing powders. Many proteins secreted by higher organisms have carbohydrates built directly into their structure and those incorporated into the cell membrane contribute to cell-cell recognition.X-ray crystallography - essentially an extremely powerful microscope - allows us to see the atoms in the 3D structures of biological macromolecules such as proteins. This knowledge is vital to an understanding of such molecules carry out their tasks. Protein-sugar complexes and glycoproteins can be studied using crystallography, but while the protein can often be seen fairly clearly in the resulting 3D structure, the sugar is often blurry because carbohydrates are often rather flexible. Interpreting the magnified image in terms of atoms and bonds can be a time consuming project, and the results somewhat subjective.The aim of this project is to provide an automated method for performing this interpretation. While automation is of value in that it frees up researcher time to concentrate on the scientific problems, a more important benefit is that it allows many possible interpretations of the magnified electron density image to be explored. In difficult cases this larger starting set of models is more likely to contain the correct answer that a single model built by a crystallographer. The different models can then be ranked to choose the best one.The structure of the protein is easily built by known methods, leaving a 'blob' of unaccounted for electron density into which the sugar must be placed. An initial set of possible structures for the sugar will be determined using existing web-based software and the Protein Data Bank (PDB). Dr. Cowtan at York University has previously written computer software which successfully identifyies the sugar rings in nucleic acids (including DNA which carries the genetic information) from their electron density alone. The 'fingerprint' technique involves the identification of shapes which are always present when the sugar is present. This approach will be modified to identify sugar rings in the carbohydrates. Each possible ring shape will be tested against the X-ray result, and neighbouring rings will be linked together. This will give a large pool of possible structures which can be ranked by automatic scoring methods based on the 3D X-ray maps and the plausibility of the chemistry.The resulting methods will be applied to two problems. The building of (1) carbohydrate molecules (such as enzyme substrates) crystallised in complex with proteins and (2) the sugars which are an integral part of glycoproteins. The software will be implemented in the ubiquitous 'Coot' graphical model building software, and will thus be made available to whole user community, both academic and commercial.The result will be a more reliable and more objective interpretation of sugars in 3D structures of macromolecules from living organisms, which in turn will enable greater understanding of the roles of these sugars in essential biological processes.