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.

解开从生物学到先进材料的大分子组装体中遇到的复杂结构对于功能理解至关重要。对于生物分子（如蛋白质和 DNA），高分辨率结构测定技术（如晶体学和冷冻电子显微镜）对于结构功能研究是不可或缺的。然而，强大的基于深度学习的高精度结构预测工具的出现给结构生物学界带来了冲击，并预示着结构研究的新时代，在这个时代，繁琐的实验高分辨率结构的常规生成可以被计算预测所取代。这些预测可以构成通过实验验证和完善设计结构功能研究的基础。称为电子顺磁共振（EPR）光谱的（生物）物理工具非常适合补充预测的结构。 EPR 检测“自旋”产生的磁性，“自旋”是不成对电子的量子力学特性。电子存在于所有物质中，并且通常配对，从而淬灭它们的磁性。然而，不成对的电子（例如自由基）支撑着许多重要的生物过程，例如光合作用、衰老和呼吸。使用 EPR，可以在纳米（十亿分之一米）尺度上确定这些自旋之间的距离。在过去的 20 年里，这些距离测量已发展成为研究复杂（生物）分子纳米世界的重要而强大的方法。分子生物学和化学允许通过选择性地引入自旋来标记生物分子中的特定位点，然后将其用作分子“信标”。引入两个这样的信标可以测量它们之间的距离。通过这种方法，可以成功绘制、验证和完善蛋白质和其他大分子的结构。在该项目中，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（包括正交铜（II））相结合-用于低浓度 RIDME 的 SLIM 标记和基于深度学习的无偏数据处理），以验证和完善逃避实验高分辨率结构测定的蛋白质的结构模型。基于纯粹的计算、高精度结构预测，可以生成信息丰富的蛋白质 EPR 构建体，其中分子信标将报告功能期间对结构和结构转变至关重要的特征。不同信标之间的距离将用于反馈到结构模型中以进行验证和细化。在功能过程中与结合伙伴的相互作用会导致结构变化，从而改变信标的距离和相对方向。通过 EPR 确定这些改变将展示这种方法的潜力，并展示其产生广泛影响的机会。人工智能正日益影响我们日常生活的许多方面。同样，深度学习彻底改变了结构研究的执行方式。该项目展示了基于深度学习的结构预测与使用 EPR 进行结构细化和验证结合的好处。这里建立的方法和工作流程是完全可转移的，扩大了 EPR 在结构功能研究中的应用范围，特别是对于目前无法达到的具有挑战性的系统（由于其尺寸、复杂性、灵活性、膜环境或可实现的量或浓度）。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，并且所提出的实验有可能揭示宿主与病原体相互作用的结构机制。只有对生物纳米世界有更多的了解，才有可能查明疾病的分子原因，并帮助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