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

生物小角X射线散射（BioSaxs）是测定蛋白质结构的重要方法。该领域的数据解释具有挑战性，需要对蛋白质的形状进行正向建模以进行预测。该项目将建立在主要主管开创的理论技术基础上，以开发一致和准确的方法来识别蛋白质结构。该项目是高度跨学科的前沿数学和实验组成部分。三个理论工作计划已经建成：模拟实验噪声。这来自实验误差和随机蛋白质运动/聚合。学生将开发和应用这些来源的统计模型，并使用Python和C++将其应用于数据。开发改进的搜索算法学生将学习应用贝叶斯抽样技术，以便全面而简约地探索难以导航的三级折叠空间。开发自动搜索后结构评估学生将开发自动评估这些预测质量的方法。首先，学生将模型预测转化为Rosetta计算蛋白质建模套件，以从模型生成可评估的蛋白质模型。其次，他们将应用拓扑度量（来自结理论）对折叠相似性的预测进行分类和比较。

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.