Hsing Michael, Byler Kendall, Cherkasov Artem
Bioinformatics Graduate Program, Faculty of Graduate Studies, University of British Columbia. 100-570 West 7th Avenue. Vancouver, BC, Canada.
Bioinformation. 2009 Oct 15;4(4):164-8. doi: 10.6026/97320630004164.
Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses.
THE SOURCE CODE AND EXECUTABLE PROGRAM OF THE HUB CLASSIFIER ARE AVAILABLE FOR DOWNLOAD AT: http://www.cnbi2.ca/hub-analysis/
中心蛋白（即在蛋白质相互作用网络（PIN）中参与大多数物理相互作用的蛋白）最近因其在介导细胞过程中的关键作用及其潜在的治疗价值而备受研究关注。如果基础的PIN是通过实验确定的，那么识别中心蛋白很直接；然而，理论上的中心蛋白预测仍然是一项极具挑战性的任务，因为区分中心蛋白与连接较少的蛋白的物理化学性质大多仍未得到表征。为了充分区分中心蛋白和非中心蛋白，我们利用了1300多个蛋白质描述符，其中一些代表定量构效关系（QSAR）参数，一些反映蛋白质的序列衍生特征，包括结构域组成和功能注释。这些蛋白质描述符与可用的蛋白质相互作用数据一起通过机器学习方法（提升树）进行处理，从而开发出能够预测四种模式生物（大肠杆菌、酿酒酵母、黑腹果蝇和智人）中高度相互作用蛋白质的中心蛋白分类器。更重要的是，通过对最相关蛋白质描述符的分析，我们能够证明中心蛋白不仅具有某些共同的物理化学和结构特征，使其与非中心对应物不同，而且它们还表现出物种特异性特征，在分析不同的PIN时应予以考虑。所开发的预测模型可用于确定四个研究物种中高度相互作用的蛋白质，以辅助未来的蛋白质组学实验和PIN分析。
中心蛋白分类器的源代码和可执行程序可在以下网址下载：http://www.cnbi2.ca/hub-analysis/
