Supramolecular structure predictions validated from sparse experimental data

从稀疏实验数据验证超分子结构预测

• 批准号: EP/X016455/1
• 负责人: Bela Bode
• 金额: $ 57.94万
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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=EP%2FX016455%2F1
• 关键词: Supramolecular; structure; predictions; validated; sparse

Unravelling the complex structures encountered in macromolecular assemblies from biology to advanced materials is paramount to functional understanding. For biomolecules (such as proteins and DNA) high-resolution structure determination techniques (such as crystallography and cryo-electron microscopy) have been indispensable for structure-function studies. However, the emergence of powerful deep learning based high-accuracy structure prediction tools has sent shock waves through the structural biology community and heralds a new era for structural studies where the routine generation of laborious experimental high-resolution structures could be replaced with computational predictions. These predictions can form the basis to design structure-function studies upon experimental validation and refinement. The (bio)physical tool called electron paramagnetic resonance (EPR) spectroscopy is ideally suited to complement predicted structures. EPR detects the magnetism arising from the "spin", a quantum mechanical property of unpaired electrons. Electrons are contained in all matter and are commonly paired, quenching their magnetism. However, unpaired electrons such as free radicals underpin many important biological processes like photosynthesis, ageing, and respiration. Using EPR, distances in-between such spins can be determined on the nanometre (one billionth of a metre) scale. Over the past 20 years, these distance measurements have developed into an important and powerful method for investigating the nanoworld of complex (bio)molecules. Molecular biology and chemistry allow labelling specific sites in biomolecules by selectively introducing spins that can then be used as molecular "beacons". Introducing two such beacons allows measurement of the distance between them. With this approach structures of proteins and other macromolecules are successfully mapped, validated and refined.In this project, deep learning-based structure prediction and modelling tools will be combined with state-of-the-art EPR techniques (including orthogonal copper(II)-SLIM labelling for low-concentration RIDME and unbiased deep learning-based data processing), to validate and refine the structural model of a protein evading experimental high-resolution structure determination. Based on purely computational, high-accuracy structure prediction it is possible to generate informative EPR constructs of the protein where the molecular beacons will report on features critical for structure and structural transitions during function. The distances between different beacons will be used to feed back into the structural model for validation and refinement. Interaction with binding partners during function leads to structural changes which alter distance and relative orientation of beacons. Determination of these alterations with EPR will show the potential of this approach and demonstrate its opportunities for wide-reaching impact.Artificial intelligence is increasingly affecting many aspects of our everyday lives. Similarly, deep learning revolutionises the way structural studies are performed. This project showcases the benefits of the marriage between deep learning-based structure prediction and structural refinement and validation using EPR. The approach and workflows established here are fully transferable, widening the application scope of EPR for structure-function studies, especially regarding challenging systems currently beyond reach (owing to their size, complexity, flexibility, membrane environment or achievable amount or concentration). Here, the approach is applied to a bacterial surface protein of unknown structure implicated in rheumatic heart disease, and proposed experiments have the potential to uncover the structural mechanism of the host-pathogen interaction. Only with a greater knowledge of the biological nanoworld will it be possible to pinpoint the molecular causes of diseases, and aid in developing prevention and treatment strategies.

揭开从生物学到高级材料的大分子组件中遇到的复杂结构对于功能理解至关重要。对于生物分子（例如蛋白质和DNA）高分辨率结构测定技术（例如晶体学和冷冻电子显微镜）对于结构功能研究是必不可少的。然而，强大的基于深度学习的高准确结构预测工具的出现通过结构生物学界引发了冲击波，并预示着结构性研究的新时代，在这种研究中，常规的经常生成的实验性实验性高分辨率结构可以用计算预测代替。这些预测可以构成实验验证和改进后设计结构功能研究的基础。 （生物）物理工具称为电子顺磁共振（EPR）光谱法非常适合补充预测的结构。 EPR检测到“自旋”（未配对电子的量子机械性能）引起的磁性。电子在所有物质中都包含，并且通常是配对的，从而消除了它们的磁性。然而，未配对的电子（例如自由基）为许多重要的生物学过程（如光合作用，衰老和呼吸）提供了基础。使用EPR，可以在纳米分数（十亿米）尺度上确定此类旋转之间的距离。在过去的20年中，这些距离测量已发展为一种重要且强大的方法，用于研究复合物（BIO）分子的纳米世界。分子生物学和化学允许通过选择性引入可以用作分子“信标”的旋转来标记生物分子中的特定位点。引入两个这样的信标可以测量它们之间的距离。借助这种方法结构，蛋白质和其他大分子的结构被成功地映射，验证和完善。在这个项目中，基于深度学习的结构预测和建模工具将与最先进的EPR技术相结合，将基于深度学习的数据和构造的模型，以及构造型号的构建，以供置式铜（包括正交铜（II）），并进行了改进，以供置于构建型号的启示）高分辨率结构确定。基于纯粹的计算，高准确性结构预测，可以生成蛋白质的信息EPR构建体，其中分子信标将报告功能过程中结构和结构过渡至关重要的特征。不同信标之间的距离将用于反馈到结构模型中以验证和改进。功能期间与结合伴侣的相互作用会导致结构变化，从而改变了信标的距离和相对方向。用EPR确定这些变化将表明这种方法的潜力，并证明其对广泛影响的机会。人工智能越来越多地影响我们日常生活的许多方面。同样，深度学习革新了进行结构研究的方式。该项目展示了使用EPR的基于深度学习的结构预测与结构的完善和验证之间的婚姻的好处。此处建立的方法和工作流是完全可转移的，从而扩大了结构功能研究的EPR的应用范围，尤其是关于当前无法触及的具有挑战性的系统（由于它们的大小，复杂性，灵活性，膜环境或可实现的量或集中度）。在这里，该方法应用于与风湿性心脏病有关的未知结构的细菌表面蛋白，并且提出的实验有可能发现宿主 - 病原体相互作用的结构机制。只有了解对生物纳米世界的知识，才有可能指出疾病的分子原因，并有助于制定预防和治疗策略。

项目成果

CRISPR antiphage defence mediated by the cyclic nucleotide-binding membrane protein Csx23

• DOI: 10.1101/2023.11.24.568546
• 发表时间: 2023-11
• 期刊: Nucleic Acids Research
• 影响因子: 14.9
• 作者: S. Grüschow;S. McQuarrie;Katrin Ackermann;Stephen McMahon;B. Bode;T. Gloster;Malcolm F. White