The Next Dimension in Proteomics

细胞、组织和有机体，包括人类，都是由包括蛋白质在内的各种分子组成的。蛋白质是“分子机器”，负责细胞的一切活动，从复制到处理营养物质。一个典型的细胞可能有由数以千计的蛋白质组成的10‘S，从事无数特定的化学“工作”，包括合成或回收其他蛋白质，因此蛋白质的组成是不断变化的。蛋白质组学领域涉及检测尽可能多的这些蛋白质，了解它们如何随时间变化以及对压力或化学处理的反应。了解这些信息将使生物科学家，从基础研究人员到临床医生，更好地了解在营养不良、疾病、健康老龄化、干旱甚至药物治疗的条件下细胞内发生的事情。开发更好的方法来准确和足够详细地测量蛋白质组是支持研究目标的关键，这将导致更好的科学、更好的技术，并最终带来更好的健康和更健康的食品。我们最近开发了一种名为二维质谱的新技术，它提供了对样本中的所有肽/蛋白质组分进行排序的能力，而不是选择几个。我们已经在几个出版物和关于酵母整体蛋白质组分析的初步数据中展示了这项技术工作，但在实现愿景之前，我们必须解决一些特殊的挑战。这项建议旨在解决这些挑战。首先，我们将优化我们的检测和片段方法，以增加蛋白质序列的覆盖率。其次，我们将充分描述这项技术的特性，以确定蛋白质修饰的灵敏度、分辨率、准确性、动态范围和定量和检测能力。第三，我们将开发一套将原始二维MS数据转换为蛋白质序列信息的算法，并将这些算法集成到基于Web的数据处理流水线中，用于自动化蛋白质组数据分析。最后，我们将在完全相同的样本集上将我们新开发的二维MS蛋白质组数据集与现有的蛋白质组方法进行比较，以清楚地展示通过以额外的维度进行蛋白质组学所提供的改进(包括已提供的和潜在的)。该项目的总体结果将是开发出使用这一令人兴奋的新技术进行最佳蛋白质组学分析的协议和方法，以及一条基于网络的数据分析管道，该管道可以将原始的二维质谱学数据处理成整个细胞和组织的蛋白质序列信息。

Cells, tissues, and organisms, including humans, are made of a variety of molecules, including proteins. Proteins are the 'molecular machines' responsible for everything a cell does, from replication to processing nutrients. A typical cell might have 10's of thousands of proteins doing myriad particular chemical 'jobs', including synthesizing or recycling other proteins, so the complement of proteins is constantly changing. The field of proteomics involves detecting as many of these proteins as possible, understanding how they change with time and in reaction to stress or chemical treatment. Knowing this information will allow bioscientists, from basic researchers to clinicians, to better understand what is occurring in the cell under conditions of malnutrition, disease, healthy aging, drought, or even pharmaceutical treatment. Developing better methods to measure the 'proteome' accurately and with sufficient detail is a key underpinning research goal which results in better science, better technology, and ultimately better health and healthier food. We have recently developed a new technology, called 2-dimensional mass spectrometry, which offers the capability to sequence all peptide/protein components in a sample, rather than a select few. We have demonstrated the technology works in several publications and in preliminary data on a whole proteomic analysis of yeast herein, but there are some particular challenges which we must address before the vision can be attained. This proposal aims to solve those challenges.Firstly, we will optimise our detection and fragmentation methods to increase protein sequence coverage. Secondly, we will fully characterise the technique to determine the sensitivity, resolution, accuracy, dynamic range, and capacity for quantitation and detection of protein modifications. Thirdly, we will develop a set of algorithms which will convert raw 2-dimensional MS data into protein sequence information and integrate those algorithms into a web-based data processing pipeline for automated proteomic data analysis. Finally, we will compare our newly developed 2-dimensional MS proteomic data sets with existing proteomic methodologies on the exact same set of samples, to clearly show the improvements (both delivered and potential) provided by doing proteomics with an extra dimension. The overall result of this project will be the development of protocols and methods for optimal proteomic analysis using this exciting new technology and a web-based data analysis pipeline which can process the raw 2-dimensional mass spectrometry data into protein sequence information for whole cells and tissues.