A versatile machine learning image recognition software for automating synchrotron Macromolecular Beamlines

用于自动化同步加速器高分子束线的多功能机器学习图像识别软件

• 批准号: BB/Z514329/1
• 负责人: David Guerra Aragao
• 金额: $ 5.19万
• 依托单位: Diamond Light Source
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FZ514329%2F1
• 关键词: versatile; machine; learning; image; recognition

Macromolecular Crystallography is one of the most used techniques for the study of the most important molecular machines in biology - Proteins - as it allows us to determine the 3D structure of these molecules and infer their function. This is particularly relevant to and has proven results in addressing human diseases ranging from genetic disorders, cancers and fighting of human pathogens. This technique is also used in agricultural and food research areas like the development of novel herbicides or drought resistant crops to address current impacts of climate change. Finally, energy storage and battery technologies have also more recently benefited from crystallography synchrotron instruments helping key manufacturing and clean growth challenges of our era. Crystallography is used by a huge range of researchers from academic to industry pharmacological companies. These researchers often send their samples to large research facilities, like synchrotrons, and then collect X-ray diffraction data remotely or use fully automated systems. With recent advances in synchrotron technology the bottlenecks have moved from the lack of intensity of the synchrotron X-rays or the speed of the detector technology to the hardware and software that makes the sample visible to X-rays by centering the sample and preparing it for data collection. A data collection on a single crystal usually takes less than 10 seconds but all the other tasks bring the time per sample to ~2 minutes. Recent advances in AI have created a paradigm shift in image analysis. There are already a few prototypes in synchrotron facilities outside of the UK using AI to improve the speed and reliability of these essential tasks. We propose to use one of the proven prototypes and further develop it for sample centring, synchrotron X-ray beamline diagnostics, and robot collision risk mitigation. This will be extremely beneficial for the MX beamlines at the UK national Synchrotron - Diamond Light Source (DLS). Many DLS sister facilities can benefit from the application of AI but lack the "know-how" to implement working AI code from scratch. This project aims to bring the technology to the UK but also facilitate the usage of AI in macromolecular crystallography beamlines across the world. Starting by integrating the French national Synchrotron - SOLEIL - trained neural network for sample holder and sample identification into an easily accessible module for use at any synchrotron worldwide would be of huge benefit. This system will then be extended by leveraging our different synchrotron databases of prior images that will be used to train even more advanced models. The coming SwissLight Source (SLS) shutdown at the Paul Scherrer Institute creates an opportunity where their staff are available for collaborations and their planned sabbatical program aligns strongly with our project vision. Finally, this project would help significantly with the roadmap for the Diamond 2 planned upgrade.

高分子晶体学是研究生物学中最重要的分子机器（蛋白质）最常用的技术之一，因为它使我们能够确定这些分子的 3D 结构并推断它们的功能。这与解决遗传性疾病、癌症和对抗人类病原体等人类疾病特别相关，并已得到证实。该技术还用于农业和食品研究领域，例如开发新型除草剂或抗旱作物，以应对当前气候变化的影响。最后，储能和电池技术最近也受益于晶体学同步加速器仪器，帮助解决我们时代的关键制造和清洁增长挑战。从学术界到工业药理学公司，大量研究人员都在使用晶体学。这些研究人员经常将样本发送到同步加速器等大型研究设施，然后远程收集 X 射线衍射数据或使用全自动系统。随着同步加速器技术的最新进展，瓶颈已经从同步加速器 X 射线强度的缺乏或探测器技术的速度转移到硬件和软件上，通过将样品居中并准备好，使样品对 X 射线可见。数据收集。单晶上的数据收集通常需要不到 10 秒的时间，但所有其他任务使每个样本的时间达到约 2 分钟。人工智能的最新进展创造了图像分析的范式转变。英国以外的同步加速器设施中已经有一些原型机使用人工智能来提高这些基本任务的速度和可靠性。我们建议使用一种经过验证的原型，并进一步开发它用于样品定心、同步加速器 X 射线束线诊断和机器人碰撞风险缓解。这对于英国国家同步加速器 - 钻石光源 (DLS) 的 MX 光束线极为有利。许多 DLS 姊妹机构可以从人工智能的应用中受益，但缺乏从头开始实施有效人工智能代码的“专业知识”。该项目旨在将该技术引入英国，同时促进人工智能在全球高分子晶体学光束线中的使用。首先将法国国家同步加速器 - SOLEIL - 经过训练的用于样品支架和样品识别的神经网络集成到一个易于访问的模块中，以便在全球任何同步加速器上使用，这将带来巨大的好处。然后，该系统将通过利用我们先前图像的不同同步加速器数据库来扩展，这些数据库将用于训练更先进的模型。 Paul Scherrer 研究所即将关闭的 SwissLight Source (SLS) 创造了一个机会，让他们的员工可以进行合作，而且他们计划的休假计划与我们的项目愿景非常一致。最后，该项目将极大地帮助制定 Diamond 2 计划升级的路线图。