大规模信号网络的结构属性分析和自动重建
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摘要
研究信号转导是了解生命活动过程的重要途径。随着实验方法的改进和实验数据的积累,对已有信号转导数据的分析和利用成为生物学家面临的一大挑战,本文旨在使用生物信息学方法解决网络结构分析、信号流走向确定等问题。
首先,本文介绍了信号网络的基本组成、一般作用方式和特点,综述了生物信息学在信号网络研究方面取得的最新进展,包括相关的数据库资源、网络的结构属性分析、自动通路重建、网络的建模和仿真方法等。
在信号网络中,衡量单个蛋白质的重要性有助于发现细胞过程中关键的蛋白质以及生物系统的薄弱环节,进一步辅助疾病诊断,具有重要的理论意义和实用价值。但是该领域中缺乏这方面的评价指标,因此本文提出了一种新的指标SigFlux,该指标与基因必要性和进化速率显著相关,表明它可以用于度量信号网络中单个蛋白质的重要性。同时,还发现高-SigFlux值、低-连接度的蛋白质在重要分子如受体和转录因子中显著富集,证明该指标能够在整个网络的范围内度量蛋白质的重要性。
其次,在信号网络中,信号流走向是蛋白质相互作用的重要属性。然而,目前高通量技术得到的大部分蛋白质相互作用都被假定为是没有方向的。为了解决这个问题,本文基于结构域定义了一个新的参数PIDS,以预测蛋白质相互作用对之间的信号流走向,用于推断信号网络中蛋白质相互作用的信号流走向。以人、小鼠、大鼠、果蝇和酵母中已知方向的蛋白质相互作用作为黄金标准阳性集,蛋白质复合体作为标准阴性集。采用5倍交叉验证对该方法进行评估,准确率为89.79%,覆盖度为48.08%,错误率为16.91%。同时,本文还研究了GO(基因本体论)功能注释以及蛋白质序列与信号流之间的关系,使用自定义函数和支持向量机方法预测成对蛋白质相互作用之间的信号流走向,进一步提高了准确率和覆盖度。
再次,本文采用贝叶斯方法整合结构域、蛋白质功能等多种数据源进行信号流走向的预测,利用综合的似然比打分值判断方向,比任意单个预测方法具有最高的可信度和最广的应用范围。在一个合适的似然比阈值16时,贝叶斯方法在人的数据集中的准确率和覆盖度分别为98.64%和67.83%,表明该方法具有相当高的应用价值。该方法已被开发成在线网页工具,提供公共服务,允许用户在自己的蛋白质相互作用数据集上推断信号通路。
最后,本文将发展的新方法用于整合的人类蛋白质相互作用网络,推断出一个高可信的有向信号网络。该网络由5,111个蛋白质和10,051对相互作用组成,包含了大量潜在的信号通路。该网络与已知数据库的重合部分具有89.23%的准确率,并且在功能注释、亚细胞定位和网络拓扑方面,呈现出与信号网络高度一致的性质。比较原有通路预测方法,本文提出了多种新的方法用于蛋白质组规模的相互作用中信号流走向预测,提供蛋白质相互作用网络的整体方向性注释。不仅能够推断出蛋白质相互作用网络中大量的潜在信号通路,而且可以提供对于信号网络的全面理解。
总之,本文采用生物信息学方法对信号网络进行了深入研究,从信号网络中单个蛋白质重要性度量到潜在信号通路推断,对于解释信号网络的作用机制提供了全新的视野,并且可以辅助实验设计和药物发现,具有重要的实用价值和广阔的应用前景。
Researches in signaling networks contribute to a deep understanding of organism living activities. With the development of experiment methods in the signal transduction field, more and more mechanisms of signaling pathways have been discovered. Analysis and utilization of such signal transduction data become a challenge for biologists. Here, we tend to explore the signaling pathways by the bioinformatics approach.
Firstly, we introduced the basic components, universal mechanisms and characteristics of signaling pathways. We summarized the current researches and latest progress in the area of bioinformatics analyses of signaling networks, including database resources, the structural properties analysis, automated pathways regeneration, the modeling and simulation of signaling networks.
Sencondly, measuring each protein’s importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. Howerver, there are relatively few methods to evaluate the importance of proteins in signaling networks. Therefore, we developed a novel network feature, SigFlux, to evaluate the importance of proteins in signaling networks. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are enriched in receptors and transcriptional factors, indicating that SigFlux can describe the importance of proteins within the context of the entire network.
Thirdly, signal flow direction is one of the most important features of the protein-protein interactions (PPIs) in signalling networks. However, almost all the outcomes of current high-throughout techniques for PPI mapping are usually non-directional. Based on the pairwise interaction domains, we defined a novel parameter Protein Interaction Directional Score (PIDS) and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. We took the protein interactions with known directions in human, mouse, rat, fly and yeast as the golden standard positive set and the non-directional protein complexes as the golden standard negative set. Using 5-fold cross-validation, our approach obtained a satisfactory performance with the accuracy 89.79%, coverage 48.08% and error ratio 16.91%. Simulatiously, we presented two other approaches to predict the signal flow in pairwise protein interactions, which are the function based on GO function annotation and the support vector machine based on protein sequence. The accuracy and coverage are improved.
Fourthly, considering the different characteristics of the above three methods, we proposed a bayesian network to integrate multiple data sources and gave likelihood ratio to evaluate the direction of signal flow between proteins. The integrated approach performed better than any prediction method based on individual source, with higher accuracy and coverage. Taking the proper threshold of likelihood ratio as 16, the bayesian method can achieve the accuracy 98.64% and coverage 67.83% in human protein interaction dataset. Simultaneously, to facilitate this strategy used by the community, we presented a web server to compute likelihood ratios of given protein interactions that allows users to infer signaling pathways from their interacting proteins.
Finally, we applied the methods to the integrated human protein interactions and established a Directional Protein Interaction Network (DPIN). The DPIN is composed of 5,111 proteins and 10,051 interactions, and involves a large amount of novel signaling pathways. The DPIN was strongly supported by the known signaling pathways literature (with the 89.23% accuracy), and further analyses on the biological annotation, subcellular localization and network topology property. All these methods we proposed could be applied to predict signal flow direction in proteome-wide protein interactions and provide a global directional annotation of the protein interaction network. These methods are powerful not only in defining unknown direction of protein interactions, but also in providing comprehensive insight into the signaling networks.
In summary, we elaborated on the signaling networks using bioinformatics methods in this paper, from the importance evaluation of each protein to automated regeneration of signalling pathways, providing a new understanding to the mechanism and principle of signaling networks. These methods can be applied to assist experiment design and drug discovery, and have a good prospect for development and application.
首先,本文介绍了信号网络的基本组成、一般作用方式和特点,综述了生物信息学在信号网络研究方面取得的最新进展,包括相关的数据库资源、网络的结构属性分析、自动通路重建、网络的建模和仿真方法等。
在信号网络中,衡量单个蛋白质的重要性有助于发现细胞过程中关键的蛋白质以及生物系统的薄弱环节,进一步辅助疾病诊断,具有重要的理论意义和实用价值。但是该领域中缺乏这方面的评价指标,因此本文提出了一种新的指标SigFlux,该指标与基因必要性和进化速率显著相关,表明它可以用于度量信号网络中单个蛋白质的重要性。同时,还发现高-SigFlux值、低-连接度的蛋白质在重要分子如受体和转录因子中显著富集,证明该指标能够在整个网络的范围内度量蛋白质的重要性。
其次,在信号网络中,信号流走向是蛋白质相互作用的重要属性。然而,目前高通量技术得到的大部分蛋白质相互作用都被假定为是没有方向的。为了解决这个问题,本文基于结构域定义了一个新的参数PIDS,以预测蛋白质相互作用对之间的信号流走向,用于推断信号网络中蛋白质相互作用的信号流走向。以人、小鼠、大鼠、果蝇和酵母中已知方向的蛋白质相互作用作为黄金标准阳性集,蛋白质复合体作为标准阴性集。采用5倍交叉验证对该方法进行评估,准确率为89.79%,覆盖度为48.08%,错误率为16.91%。同时,本文还研究了GO(基因本体论)功能注释以及蛋白质序列与信号流之间的关系,使用自定义函数和支持向量机方法预测成对蛋白质相互作用之间的信号流走向,进一步提高了准确率和覆盖度。
再次,本文采用贝叶斯方法整合结构域、蛋白质功能等多种数据源进行信号流走向的预测,利用综合的似然比打分值判断方向,比任意单个预测方法具有最高的可信度和最广的应用范围。在一个合适的似然比阈值16时,贝叶斯方法在人的数据集中的准确率和覆盖度分别为98.64%和67.83%,表明该方法具有相当高的应用价值。该方法已被开发成在线网页工具,提供公共服务,允许用户在自己的蛋白质相互作用数据集上推断信号通路。
最后,本文将发展的新方法用于整合的人类蛋白质相互作用网络,推断出一个高可信的有向信号网络。该网络由5,111个蛋白质和10,051对相互作用组成,包含了大量潜在的信号通路。该网络与已知数据库的重合部分具有89.23%的准确率,并且在功能注释、亚细胞定位和网络拓扑方面,呈现出与信号网络高度一致的性质。比较原有通路预测方法,本文提出了多种新的方法用于蛋白质组规模的相互作用中信号流走向预测,提供蛋白质相互作用网络的整体方向性注释。不仅能够推断出蛋白质相互作用网络中大量的潜在信号通路,而且可以提供对于信号网络的全面理解。
总之,本文采用生物信息学方法对信号网络进行了深入研究,从信号网络中单个蛋白质重要性度量到潜在信号通路推断,对于解释信号网络的作用机制提供了全新的视野,并且可以辅助实验设计和药物发现,具有重要的实用价值和广阔的应用前景。
Researches in signaling networks contribute to a deep understanding of organism living activities. With the development of experiment methods in the signal transduction field, more and more mechanisms of signaling pathways have been discovered. Analysis and utilization of such signal transduction data become a challenge for biologists. Here, we tend to explore the signaling pathways by the bioinformatics approach.
Firstly, we introduced the basic components, universal mechanisms and characteristics of signaling pathways. We summarized the current researches and latest progress in the area of bioinformatics analyses of signaling networks, including database resources, the structural properties analysis, automated pathways regeneration, the modeling and simulation of signaling networks.
Sencondly, measuring each protein’s importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. Howerver, there are relatively few methods to evaluate the importance of proteins in signaling networks. Therefore, we developed a novel network feature, SigFlux, to evaluate the importance of proteins in signaling networks. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are enriched in receptors and transcriptional factors, indicating that SigFlux can describe the importance of proteins within the context of the entire network.
Thirdly, signal flow direction is one of the most important features of the protein-protein interactions (PPIs) in signalling networks. However, almost all the outcomes of current high-throughout techniques for PPI mapping are usually non-directional. Based on the pairwise interaction domains, we defined a novel parameter Protein Interaction Directional Score (PIDS) and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. We took the protein interactions with known directions in human, mouse, rat, fly and yeast as the golden standard positive set and the non-directional protein complexes as the golden standard negative set. Using 5-fold cross-validation, our approach obtained a satisfactory performance with the accuracy 89.79%, coverage 48.08% and error ratio 16.91%. Simulatiously, we presented two other approaches to predict the signal flow in pairwise protein interactions, which are the function based on GO function annotation and the support vector machine based on protein sequence. The accuracy and coverage are improved.
Fourthly, considering the different characteristics of the above three methods, we proposed a bayesian network to integrate multiple data sources and gave likelihood ratio to evaluate the direction of signal flow between proteins. The integrated approach performed better than any prediction method based on individual source, with higher accuracy and coverage. Taking the proper threshold of likelihood ratio as 16, the bayesian method can achieve the accuracy 98.64% and coverage 67.83% in human protein interaction dataset. Simultaneously, to facilitate this strategy used by the community, we presented a web server to compute likelihood ratios of given protein interactions that allows users to infer signaling pathways from their interacting proteins.
Finally, we applied the methods to the integrated human protein interactions and established a Directional Protein Interaction Network (DPIN). The DPIN is composed of 5,111 proteins and 10,051 interactions, and involves a large amount of novel signaling pathways. The DPIN was strongly supported by the known signaling pathways literature (with the 89.23% accuracy), and further analyses on the biological annotation, subcellular localization and network topology property. All these methods we proposed could be applied to predict signal flow direction in proteome-wide protein interactions and provide a global directional annotation of the protein interaction network. These methods are powerful not only in defining unknown direction of protein interactions, but also in providing comprehensive insight into the signaling networks.
In summary, we elaborated on the signaling networks using bioinformatics methods in this paper, from the importance evaluation of each protein to automated regeneration of signalling pathways, providing a new understanding to the mechanism and principle of signaling networks. These methods can be applied to assist experiment design and drug discovery, and have a good prospect for development and application.
引文
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