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

蛋白质相互作用网络已经成为理解生物模式生物分子表型的重要工具。尽管蛋白质相互作用数据的质量和完整性方面存在众所周知的问题，但这样的数据正在不断增加。这些数据主要来自一些特征良好的模式生物，特别是酿酒酵母。对于其他重要的生物，数据是稀少的。实验绘制相互作用图谱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体中相互作用数据的匮乏。已经提出了一些使用同源性来预测蛋白质跨物种相互作用的计算方法。然而，这些方法不能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，他们最多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一严重限制。这些工具将使我们能够使用复杂的生物信息学、统计学和比较论点来可靠和全面地预测蛋白质相互作用数据。这些将被应用于致病真菌光滑假丝酵母，并在其中进行验证，光滑假丝酵母是人类最重要的真菌病原体之一。新的办法将被纳入一个协调一致的框架，以便合理地绘制相互作用图。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结合，来区别和改进现有的方法。此外，我们还将建立一个实验衍生的蛋白质相互作用支架，可以用来指导和验证理论预测。这项联合的计算机内和实验研究将开发一种一般的、合理的方法来绘制相互作用图，特别是在与研究得很好的模式生物(例如，来自黑腹果蝇的冈比亚按蚊和埃及伊蚊；或来自秀丽线虫的线虫)的生物体中。我们将进一步探索这种类型的网络数据对功能和进化分析的额外好处。除了将光肩星天牛发展为比较系统生物学的模式生物外，这种方法还将进一步突出比较方法在综合系统生物学中的作用。

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.