A rational in silico and experimental approach to mapping interactomes applied to Candida glabrata

蛋白质相互作用网络已成为了解生物模式生物分子表型的重要工具。尽管在蛋白质相互作用数据的质量和完整性方面存在着众所周知的问题，但这类数据的数量正在不断增加。这一数据主要来自于一些具有良好特征的模式生物，特别是酿酒酵母。对于其他重要的生物，数据是稀疏的。相互作用组的实验映射是劳动密集型和昂贵的，导致包括人类在内的绝大多数生物缺乏相互作用数据。已经提出了许多计算方法，这些方法使用同源性来预测跨物种的蛋白质相互作用。然而，这些方法无法预测不同生物体之间的差异。由于他们对同源性的基本假设，他们充其量只能建立一个普遍共享的蛋白质-蛋白质相互作用的支架。在这里，我们将开发新的工具来克服这个严重的限制。这些工具将允许我们使用复杂的生物信息学、统计学和比较论证可靠和全面地预测蛋白质相互作用数据。这些将应用于致病真菌念珠菌，人类最重要的真菌病原体之一，并在其中得到验证。新的方法将被整合到一个连贯的框架中，以合理地映射相互作用组。我们的方法将通过探索一系列不同的统计模型和分类器，并通过干法和湿法之间的紧密集成，不同于现有的方法，并对其进行改进。我们将进一步建立一个实验衍生的蛋白质相互作用支架，可用于指导和验证理论预测。这项联合计算机和实验研究将开发一种通用的、合理的方法来绘制相互作用组，特别是在与已被充分研究的模式生物（例如，来自黑腹果蝇的冈比亚按蚊和埃及伊蚊；或来自秀丽隐杆线虫的briggsae隐杆线虫）有相当密切关系的生物中。我们将进一步探讨这种类型的网络数据在功能和进化分析中的附加好处。这一研究方法将进一步突出比较研究方法在综合系统生物学中的作用，并将使光棘藓成为比较系统生物学的模式生物。

Protein interaction networks have become important tools in the understanding of molecular phenotypes of biological model organisms. Although there are well known problems regarding the quality and completeness of protein-interaction data, a constantly growing amount of such data is being assembled. This data is primarily derived from a few well characterized model organisms, notably Saccharomyces cerevisiae. For other biological important organisms data is sparse. Experimental mapping of interactomes is labour intensive and expensive, resulting in a dearth of interaction data in the vast majority of organisms, including humans. A number of computational approaches have been proposed which use homology to predict protein interactions across species. These approaches cannot, however, predict differences between different organisms. Because of their underlying assumptions about homology, at best they are restricted to establishing a scaffold of protein-protein interactions that are universally shared. Here we will develop novel tools that overcome this severe limitation. These tools will allow us to predict reliably and comprehensively protein interaction data using sophisticated bioinformatics, statistical and comparative arguments. These will be applied to, and validated in, the pathogentic fungus Candida glabrata, one of the most important fungal pathogens of humans. The new approaches will be integrated into a coherent framework for the rational mapping of interactomes. Our approach will differ from and improve upon existing approaches by exploring a range of different statistical models and classifiers and through the close integration between dry and wet approaches. We will furthermore establish an experimentally derived scaffold for protein interactions which can be used to guide as well as validate the theoretical predictors. This joint in-silico and experimental study will develop a general, rational approach to mapping interactomes, especially in organisms that are reasonably closely related to well studied model organisms (e.g. Anopheles gambiae and Aedes aegypti from Drosophila melanogaster; or Caenorhabditis briggsae from Caenorhabditis elegans). We will furthermore explore the added benefit of this type of network data for functional and evolutionary analyses. In addition to developing C.glabrata as a model organism for comparative systems biology, this approach will further highlight the role of comparative approaches in integrative systems biology.