Max Planck Institute for Molecular Genetics (MPIMG), Otto-Warburg Laboratory, Berlin, Germany.
Proteomics Clin Appl. 2013 Dec;7(11-12):727-32. doi: 10.1002/prca.201300039. Epub 2013 Oct 29.
Protein-protein interaction networks are typically generated in standard cell lines or model organisms as it is prohibitively difficult to record large interaction datasets from specific tissues or disease models at a reasonable pace. Although the interaction data are of high confidence, they thus do not reflect in vivo relationships as such. A wealth of physiologically relevant protein information, obtained under different conditions and from different systems, is available including information on genetic variation, protein levels, and PTMs. However, these data are difficult to assess comprehensively because the relationships between the entities remain elusive from the measurements. Here, we exemplarily highlight recent studies that gained deeper insight from genetic variation, protein, and PTM measurements using interaction information pointing toward the importance and potential of interaction networks for the interpretation of sequencing and proteomics data.
蛋白质-蛋白质相互作用网络通常在标准细胞系或模式生物中生成，因为以合理的速度从特定组织或疾病模型中记录大型相互作用数据集非常困难。尽管这些相互作用数据具有很高的可信度，但它们并不能反映特定组织或疾病模型中的体内关系。目前已经有大量与生理相关的蛋白质信息，这些信息来自不同的条件和系统，包括遗传变异、蛋白质水平和 PTM 等信息。然而，这些数据很难进行全面评估，因为从测量结果中很难找到实体之间的关系。在这里，我们举例说明了最近的一些研究，这些研究利用相互作用信息从遗传变异、蛋白质和 PTM 测量中获得了更深入的见解，这些信息表明相互作用网络对于解释测序和蛋白质组学数据的重要性和潜力。
