Developing reliable ab-inito software for the interpretation of protein structure from BioSaxs data.

点击翻译按钮获取中文摘要

Biological small-angle X-ray scattering (BioSaxs) is an important method for determining protein structure. Data interpretation in this field is challenging, requiring forward modelling of the protein's shape to make a predictions.The project will build on theoretical techniques pioneered by the primary supervisor to develop consistent and accurate methods for identifying protein structures. The project is highly interdisciplinary with cutting-edge mathematical and experimental components.Three theoretical work plans have been constructed:Modelling experimental noise. This comes from experimental error and random protein motion/polymerisation. The student will develop and apply statistical models for these sources and apply them to data using Python, and C++.Developing improved search algorithms The student will learn to apply Bayesian sampling techniques so that the hard to navigate the tertiary fold space is explored comprehensively and parsimoniously.Development of automated post-search structural assessment The student will develop methods to automatically rate the quality of these predictions. First, the student translate the model predictions into the Rosetta computational protein modelling suite to generate assessable protein models from the model. Second, they will apply topological metrics (from knot theory) for classifying and comparing predictions for fold similarity.