Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 830011, Urumqi, China.
School of Computer Science and Technology, Wuhan University of Technology, 430070, Wuhan, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab036.
Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suffer from the disadvantages of being time-consuming and labor-intensive. The results obtained are often not robust and considerably uncertain. Due recently to advances in high-throughput technologies, a large amount of proteomics data has been collected and this presents a significant opportunity and also a challenge to develop computational models to predict protein-protein interactions (PPIs) based on these data. In this paper, we present a comprehensive survey of the recent efforts that have been made towards the development of effective computational models for PPI prediction. The survey introduces the algorithms that can be used to learn computational models for predicting PPIs, and it classifies these models into different categories. To understand their relative merits, the paper discusses different validation schemes and metrics to evaluate the prediction performance. Biological databases that are commonly used in different experiments for performance comparison are also described and their use in a series of extensive experiments to compare different prediction models are discussed. Finally, we present some open issues in PPI prediction for future work. We explain how the performance of PPI prediction can be improved if these issues are effectively tackled.
蛋白质相互作用在细胞的许多生物过程中发挥着关键作用。尽管很有前景，但实验室实验通常存在耗时和劳动密集的缺点。得到的结果往往不够稳健，而且相当不确定。由于最近高通量技术的进步，已经收集了大量的蛋白质组学数据，这为开发基于这些数据预测蛋白质-蛋白质相互作用（PPIs）的计算模型提供了一个重大的机会和挑战。在本文中，我们全面调查了最近在开发有效的 PPI 预测计算模型方面所做的努力。该调查介绍了可用于学习预测 PPIs 的计算模型的算法，并将这些模型分为不同的类别。为了了解它们的相对优点，本文讨论了不同的验证方案和指标来评估预测性能。还描述了在不同实验中用于性能比较的常用生物数据库，并讨论了它们在一系列广泛实验中用于比较不同预测模型的使用情况。最后，我们提出了 PPI 预测中的一些开放性问题，以供未来工作参考。我们解释了如果有效地解决这些问题，如何提高 PPI 预测的性能。
