Proteomics become an important research area of interests in life science after the completion of the human genome project. This scientific is to study the characteristics of proteins at the large-scale data level, and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level. A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies. Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era, such as protein-protein interactions (PPIs). In this review, we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects. First, we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources. Second, we describe the state-of-the-art computational methods recently proposed on this topic. Finally, we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.

中文翻译：

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蛋白质-蛋白质相互作用的计算预测的当前趋势调查

在完成人类基因组计划后，蛋白质组学已成为生命科学领域的重要研究领域。这项科学的工作是在大规模数据级别研究蛋白质的特征，然后从蛋白质级别全面了解疾病的发生和细胞代谢过程。蛋白质组学的关键问题是如何有效分析高通量技术产生的大量蛋白质数据。低成本和短周期的计算技术正成为解决后基因组时代一些重要问题（例如蛋白-蛋白相互作用（PPI））的首选方法。在本文中，我们重点介绍了检测PPI的计算方法，并从多个方面展示了该关键领域的最新进展。第一，我们详细分析了预测PPI的计算方法面临的几个挑战，并总结了可用的PPI数据源。其次，我们描述了最近在该主题上提出的最新计算方法。最后，我们讨论了一些可以促进PPI预测和计算蛋白质组学发展的重要技术。