Yong Chern Han, Wong Limsoon
Graduate School for Integrative Sciences and Engineering, National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore.
J Bioinform Comput Biol. 2015 Apr;13(2):1571001. doi: 10.1142/S0219720015710018. Epub 2015 Jan 7.
Protein interactions and complexes behave in a dynamic fashion, but this dynamism is not captured by interaction screening technologies, and not preserved in protein-protein interaction (PPI) networks. The analysis of static interaction data to derive dynamic protein complexes leads to several challenges, of which we identify three. First, many proteins participate in multiple complexes, leading to overlapping complexes embedded within highly-connected regions of the PPI network. This makes it difficult to accurately delimit the boundaries of such complexes. Second, many condition- and location-specific PPIs are not detected, leading to sparsely-connected complexes that cannot be picked out by clustering algorithms. Third, the majority of complexes are small complexes (made up of two or three proteins), which are extra sensitive to the effects of extraneous edges and missing co-complex edges. We show that many existing complex-discovery algorithms have trouble predicting such complexes, and show that our insight into the disparity between the static interactome and dynamic protein complexes can be used to improve the performance of complex discovery.
蛋白质相互作用和复合物以动态方式存在，但这种动态性无法通过相互作用筛选技术捕捉到，也无法在蛋白质-蛋白质相互作用（PPI）网络中得以保留。通过分析静态相互作用数据来推导动态蛋白质复合物会带来若干挑战，我们确定其中有三个挑战。首先，许多蛋白质参与多个复合物，导致重叠复合物嵌入在PPI网络的高度连接区域内。这使得准确界定此类复合物的边界变得困难。其次，许多特定条件和位置的PPI未被检测到，导致形成稀疏连接的复合物，而聚类算法无法识别这些复合物。第三，大多数复合物是小复合物（由两个或三个蛋白质组成），它们对外来边缘和缺失的共复合物边缘的影响格外敏感。我们表明，许多现有的复合物发现算法在预测此类复合物时存在困难，并表明我们对静态相互作用组与动态蛋白质复合物之间差异的洞察可用于提高复合物发现的性能。
