基于PPI网络的关键蛋白质的高效预测算法

doi:10.11896/j.issn.1002-137X.2016.11A.004

摘要/Abstract

摘要： 关键蛋白质对于细胞生活是不可缺少的,识别关键蛋白质可以帮助了解细胞生活的最小需求,同时对药物设计也有非常大的作用。随着高通量技术的发展,人们可得到越来越多的蛋白质-蛋白质相互作用(PPI)的数据,这就使得可以在网络层次上来研究关键蛋白质。目前,学术界已经提出了一系列的计算方法来识别关键蛋白质,但这些方法并没有完全解决蛋白质相互作用数据的假阳性问题。除此之外,现有方法一般只考虑了网络的拓扑结构,对于生物信息的考虑,目前还是比较欠缺的。蛋白质对于人类细胞的生命活动不仅仅与网络拓扑结构有关,还和蛋白质在网络上的生物信息相关。因此,针对以上问题,提出了一种高效的预测关键蛋白质的新方法EPP(Essential Proteins Predict),该方法通过计算蛋白质在PPI网络中的重要性来进行预测,蛋白质的重要性越高,成为关键蛋白质的可能性就越大。取重要性排名前P%的蛋白质作为关键蛋白质,在进行蛋白质重要性的计算时,综合考虑语义相似度及可信度因素,以综合考虑网络的拓扑结构与蛋白质本身的生物信息。实验结果表明,与其他传统方法相比,提出的新方法复杂度较低,且能够识别出更多的关键蛋白质,并且其统计指标也高于其他的方法。

关键词: 关键蛋白质,GO,语义相似度

Abstract: The essential protein is indispensable for cell life,so it is very helpful for us to understand the minimum requirements of cellular life and design the drug through identifying essential protein.With the development of high-throughput technologies,more and more protein- protein interactions (PPI) data has been obtained,which makes it possible to study essential protein from the network level.At present there are already a number of computational methods proposed for essential proteins identification,but these methods do not solve the PPI data false positive issues.In addition,existing methods generally just consider the topology of the network not considering biological information of protein on the network,and is still relatively lacking.Protein for human life activities of cells not only related to the topology of the network,but also related with protein biological information on the network.To solve the above problems,this paper presented an efficient new method to predict essential protein called EPP (Essential Proteins Predict).The algorithm predicts essential proteins through computing the importance score of protein in the PPI network,the higher importance score of protein is, the protein is more likely to be essential.We take the importance of rank P% of the protein as essential protein.When computing the importance score of essential protein,we synthetically considered the semantic similarity and credibility factors.Our method has low complexity,and considers not only the topology of the network but also the biological meaning of protein itself.Experimental results show that,compared with other conventional methods,our method can identify more essential protein,and its statistical indicators is also higher than other methods.

Key words: Essential protein,GO,Semantic similarity

洪海燕,刘维. 基于PPI网络的关键蛋白质的高效预测算法[J]. 计算机科学, 2016, 43(Z11): 16-20. https://doi.org/10.11896/j.issn.1002-137X.2016.11A.004

HONG Hai-yan and LIU Wei. Efficient Prediction Method of Essential Proteins Based on PPI Network[J]. Computer Science, 2016, 43(Z11): 16-20. https://doi.org/10.11896/j.issn.1002-137X.2016.11A.004

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