Automated modelling of protein-nucleotide complexes using X-ray data and AlphaFold models

使用 X 射线数据和 AlphaFold 模型对蛋白质-核苷酸复合物进行自动建模

• 批准号: 2741770
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2741770
• 关键词: Automated; modelling; protein; nucleotide; complexes

Background:Building an atomic model into an electron density map is a key stage in the solution of 3D structures by X-ray or EM methods. The new AlphaFold AI software from Google provides theoretical models which can be used to start the model building process for simple protein structures, but not complexes involving other proteins and nucleic acids. YSBL have a history of software for automated model building which may be able to fill this gap.Objectives:The aim of the project is to investigate how to combine AlphaFold models for different protein components of a complex with experimental observations using the YSBL-developed 'Buccaneer' and 'Nautilus' software to build atomic models for large complexes.Novelty:Previously, X-ray crystallography relied upon either homologous models or complex additional experiments to solve the 'crystallographic phase problem'. In 2021, the AlphaFold AI software was released which provides accurate theoretical models for a wide range of protein molecules based only on their known sequence; while there is great variance in their results, their best models are on-par with those obtained experimentally. Furthermore, the AlphaFold Protein Structure Database provides pre-calculated models for 20,000 humanproteins and many of 19 other biologically relevant organisms. Both methods and data are so new that we are only beginning to discover how best to use them, and so this provides fertile ground for method development with immediate and wide-ranging impact.Timeliness:X-ray crystallographic structure solution is increasingly conducted by non-specialists, who often rely on software to produce an accurate structure with limited manual validation. It is therefore increasingly important that the software produces the most complete and accurate model possible. The possibilities recently opened by the AlphaFold method and its associated model database present a timely opportunity for a PhD student to make several world-leading contributions in a time scale commensurate of a PhD program.Experimental Approach:The first step will be to assemble a library of solved protein-nucleotide test structures from public resources including the Protein Data Bank, and pick appropriate AlphaFold models for the protein components. The existing model building software will be tested on these structures to build the missing components, in order to identify where new work is required. Algorithm development and optimisation will focus on improving these areas. Experience in computer programming is a prerequisite.

背景：将原子模型构建为电子密度图是通过 X 射线或 EM 方法求解 3D 结构的关键阶段。谷歌的新 AlphaFold AI 软件提供了理论模型，可用于启动简单蛋白质结构的模型构建过程，但不适用于涉及其他蛋白质和核酸的复合物。 YSBL 拥有自动模型构建软件的历史，可能能够填补这一空白。目标：该项目的目的是研究如何将复合物不同蛋白质成分的 AlphaFold 模型与实验观察相结合，使用 YSBL 开发的“Buccaneer”和“Nautilus”软件为大型复合物构建原子模型。新颖性：以前，X 射线晶体学依赖于同源模型或同源模型。 复杂的附加实验来解决“晶体相问题”。 2021年，AlphaFold AI软件发布，该软件仅根据已知序列为多种蛋白质分子提供准确的理论模型；虽然他们的结果存在很大差异，但他们的最佳模型与实验获得的模型相当。此外，AlphaFold 蛋白质结构数据库提供了 20,000 种人类蛋白质和 19 种其他生物相关生物体中的许多的预先计算模型。方法和数据都很新，我们才刚刚开始探索如何最好地使用它们，因此这为具有直接和广泛影响的方法开发提供了肥沃的土壤。 及时性：X射线晶体结构解决方案越来越多地由非专家进行，他们通常依靠软件在有限的手动验证下产生精确的结构。因此，软件生成尽可能完整和准确的模型变得越来越重要。 AlphaFold 方法及其相关模型数据库最近带来的可能性为博士生提供了一个及时的机会，使其能够在与博士课程相当的时间范围内做出多项世界领先的贡献。实验方法：第一步是从包括蛋白质数据库在内的公共资源中组装一个已解决的蛋白质-核苷酸测试结构库，并为蛋白质成分选择适当的 AlphaFold 模型。现有的模型构建软件将在这些结构上进行测试，以构建缺失的组件，以确定需要进行新工作的地方。算法开发和优化将重点改进这些领域。计算机编程经验是先决条件。