High throughput protein analysis in complex matrices using ultrafast 2D-IR spectroscopy

使用超快 2D-IR 光谱法对复杂基质进行高通量蛋白质分析

• 批准号: 2885397
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2885397
• 关键词: throughput; protein; analysis; complex; matrices

Background: The continuous development of ultrafast laser technology over the last decade has delivered the ability to acquire information-rich 2D-IR spectroscopy datasets at ever increasing rates. Currently, one 2D-IR spectrum can be acquired in less than a minute using 100 kHz repetition rate laser systems, while the advent of next generation technology at national facilities is expected to decrease this further. However, whilst the efficiency of data collection has increased enormously, the barrier to 2D-IR generating impact in biological and biomedical arenas is the efficient and effective handling of large amounts of spectral data. This challenge is twofold 1) First and foremost is the need to obtain, process and present data at a rate that is comparable to the measurement time (building on our recent work J. Chem. Phys. 157, 205102 (2022)) 2) There is a now a realisation that advanced computational 'big-data' methodologies hold the key to unlocking the complexities of 2D-IR datasets. Solving these two issues will require not only automated sampling systems to record data libraries but also the application of machine Learning or Artificial Intelligence-driven data analysis routines.Another development that we seek to capitalise on is our innovative work that has enabled us to move away from the conventional methods of obtaining 2D-IR spectra in D2O to avoid natural water interference that obscures protein spectral signatures. We have taken a world-lead in the analysis of protein structure and dynamics in native solvents (e.g. Chemical Science, 10, 6448-6456 (2019)). This has led to proof-of-concept applications of 2D-IR for high throughput protein-drug screening and molecular analysis of blood serum samples, including drug binding to the serum albumin protein in blood serum (Analyst, 147, 3464-3469 (2022).ObjectivesBuilding on the recent installation of world-leading UKRI-funded laser technology at the University of York and the development of automated sample that is handling currently underway, the aim is to establish automated data processing, visualisation and high-level analysis methods for 2D-IR spectroscopy. Testing and development phases at York in collaboration with the Central Laser Facility (CLF) will then progress to technology transfer to the CLF for the benefit of the user community. Scientifically, we will focus on the development of biomolecular (protein and nucleic acid) analysis in complex biological matrices (biofluids, soft tissue and bone) in order to meet varying challenges in sample presentation, data acquisition and analysis. Access to the York instrument will confer the ability to acquire large data libraries to support ML/AI data analysis methods. The student will provide a key state of readiness to optimise uptake of the new CLF technology.

背景：在过去的十年中，超快激光技术的不断发展提供了以越来越快的速度获取信息丰富的2D-IR光谱数据集的能力。目前，使用100 kHz重复频率激光系统可以在不到一分钟的时间内获得一个2D-IR光谱，而国家设施中下一代技术的出现预计将进一步减少这一数字。然而，虽然数据收集的效率已经大大提高，但2D-IR在生物和生物医学领域产生影响的障碍是高效和有效地处理大量光谱数据。这一挑战是双重的1)首先是需要以与测量时间相当的速度获取、处理和呈现数据(基于我们最近的工作J.太棒了。205102(2022年)2)现在人们意识到，先进的计算‘大数据’方法是解锁2D-IR数据集复杂性的关键。解决这两个问题不仅需要自动采样系统来记录数据库，还需要机器学习或人工智能驱动的数据分析例程的应用。我们寻求利用的另一个发展是我们的创新工作，使我们能够摆脱在D2O中获得2D-IR光谱的传统方法，以避免自然水干扰模糊蛋白质光谱特征。我们在分析天然溶剂中的蛋白质结构和动力学方面处于世界领先地位(例如，《化学科学》，10,6448-6456(2019))。这导致了2D-IR在高通量蛋白质药物筛选和血清样本分子分析中的概念验证应用，包括药物与血清中血清白蛋白的结合(分析师，147,3464-3469(2022))。目的在约克大学最近安装的世界领先的UKRI资助的激光技术和目前正在进行的自动化样品开发的基础上，目标是建立2D-IR光谱的自动化数据处理、可视化和高级分析方法。约克的测试和开发阶段将与中央激光设施合作，然后向激光中心转让技术，以造福于用户群体。科学上，我们将重点发展复杂生物基质(生物流体、软组织和骨骼)中的生物分子(蛋白质和核酸)分析，以应对样品呈现、数据采集和分析方面的各种挑战。对约克仪器的访问将使其能够获得大型数据库，以支持ML/AI数据分析方法。学生将提供一个关键的准备状态，以优化对新CLF技术的采用。