van Noort Vera, Snel Berend, Huynen Martijn A
Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Toernooiveld, 6525 ED Nijmegen, The Netherlands.
Genome Biol. 2007;8(9):R197. doi: 10.1186/gb-2007-8-9-r197.
In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions. Qualitative labels are essential for the molecular biologist to experimentally verify predicted interactions.
Of the individual protein-protein interaction prediction methods, some can predict physical interactions without producing other types of interactions. None of the methods can specifically predict metabolic interactions. We have constructed an 'omics evidence landscape' that combines all sources of evidence for protein interactions from various types of omics data for Saccharomyces cerevisiae. We explore this evidence landscape to identify areas with either only metabolic or only physical interactions, allowing us to specifically predict the nature of new interactions in these areas. We combine the datasets in ways that examine the whole evidence landscape and not only the highest scoring protein pairs in both datasets and find specific predictions.
The combination of evidence types in the form of the evidence landscape allows for qualitative labels to be inferred and placed on the predicted protein interaction network of S. cerevisiae. These qualitative labels will help in the biological interpretation of gene networks and will direct experimental verification of the predicted interactions.
在后基因组时代，已经开发了各种功能基因组学、蛋白质组学和计算技术来阐明蛋白质相互作用网络。虽然其中一些技术特定于某种类型的相互作用，但大多数技术预测的是相互作用的混合体。定性标签对于分子生物学家通过实验验证预测的相互作用至关重要。
在各种蛋白质-蛋白质相互作用预测方法中，有些方法可以预测物理相互作用而不产生其他类型的相互作用。没有一种方法能够专门预测代谢相互作用。我们构建了一个“组学证据图谱”，它整合了来自酿酒酵母各种组学数据中蛋白质相互作用的所有证据来源。我们探索这个证据图谱，以识别仅存在代谢相互作用或仅存在物理相互作用的区域，从而使我们能够专门预测这些区域中新相互作用的性质。我们以检查整个证据图谱而非仅两个数据集中得分最高的蛋白质对的方式组合数据集，并得出具体预测结果。
以证据图谱形式呈现的证据类型组合，使得能够推断出定性标签并将其置于酿酒酵母预测的蛋白质相互作用网络上。这些定性标签将有助于基因网络的生物学解释，并指导对预测相互作用的实验验证。
