Peptide aptamer optical protein detection

肽适配体光学蛋白质检测

• 批准号: BB/F011032/1
• 负责人: Jason Davis
• 金额: $ 40.11万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FF011032%2F1
• 关键词: Peptide; aptamer; optical; protein; detection

Our bodies are made up of trillions of cells, which contain billions of proteins. Proteins are the workhorses of a cell, and until we understand how they work together, we will not be able to understand life at the level of each cell, nor will we be able to identify the changes that occur in triggering a disease. While the sequencing of the human genome has given us a catalogue of these proteins, we still lack the tools that allow us to study them both individually- is protein X expressed in this cell?- and collectively- do proteins X and Y, which are both expressed in a cell, actually interact with each other? There are two major hurdles to such studies. The first is that we need probes for each protein that allow us to detect a given protein even in the presence of the billions of other protein molecules in a cell extract. The second is that we need a way to sense when a target protein has bound to its specific probe- proteins are so small that any signal they generate is minute. One way around this has been to add labels to proteins that are capable of generating very large signals, but this solution has its own problems, as the label can modify the behaviour of the protein, for example to prevent a biologically-significant binding event, or to create a new, irrelevant interaction. We have solved both problems using a technique called protein engineering to create artificial proteins that specifically recognise target proteins, and that are sufficiently robust that they can be attached to sensing surfaces to generate label-free electrical or optical signals. Recent advances in our understanding of molecular fluorescence and the optical properties of nanometre-scale structures offer much in the generation of sensitive sensory interfaces. The goal of this project is to integrate receptive artificial proteins with these interfaces and identify the best means of applying our proof-of-concept work to the generation of detection methods both compatible with the needs of researchers but also enabling currently unanswerable questions to be answered. Such questions range from asking what is the smallest number of copies of protein that can be detected in a single cell, to what is the largest number of different proteins that can be detected simultaneously using a technology that is affordable to most biological/clinical labs.

我们的身体由数万亿个细胞组成，其中含有数十亿种蛋白质。蛋白质是细胞的主要组成部分，在我们了解它们如何协同工作之前，我们将无法理解每个细胞水平上的生命，也无法识别引发疾病的变化。虽然人类基因组的测序为我们提供了这些蛋白质的目录，但我们仍然缺乏能够让我们单独研究它们的工具-蛋白质X在这个细胞中表达吗？总的来说，在细胞中表达的蛋白质X和Y，是否真的相互作用？这类研究有两个主要障碍。首先，我们需要针对每种蛋白质的探针，使我们能够检测到给定的蛋白质，即使在细胞提取物中存在数十亿其他蛋白质分子的情况下。第二，我们需要一种方法来检测目标蛋白质何时与其特异性探针结合-蛋白质非常小，它们产生的任何信号都是微小的。解决这个问题的一种方法是将标签添加到能够产生非常大信号的蛋白质上，但这种解决方案有其自身的问题，因为标签可以改变蛋白质的行为，例如防止生物学上重要的结合事件，或创建新的不相关的相互作用。我们已经解决了这两个问题，使用一种称为蛋白质工程的技术来创造人工蛋白质，这种蛋白质可以特异性地识别目标蛋白质，并且足够强大，可以附着在传感表面上，产生无标记的电信号或光信号。我们对分子荧光和纳米尺度结构的光学性质的理解的最新进展在敏感的感觉界面的产生方面提供了很多。该项目的目标是将接受性人工蛋白质与这些界面相结合，并确定将我们的概念验证工作应用于生成检测方法的最佳方法，这些方法既符合研究人员的需求，又能够回答目前无法回答的问题。这些问题的范围从询问在单个细胞中可以检测到的蛋白质的最小拷贝数，到使用大多数生物/临床实验室负担得起的技术可以同时检测到的不同蛋白质的最大数量。