基于图和复杂网络理论的蛋白质相互作用数据分析与应用研究
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摘要
蛋白质-蛋白质相互作用在生物体的生命活动中扮演着极其重要的作用,几乎涉及到每一个生理过程。高通量实验鉴定技术和计算预测方法的快速发展使得直接和间接来源的大规模蛋白质相互作用数据不断累积。然而,大规模蛋白质相互作用数据中较高比例的假阳性和假阴性“噪声”严重影响了相互作用数据的质量。生物信息学方法能够从已有的数据和知识出发,通过计算的方法系统评估和预测蛋白质相互作用数据的假阳性和假阴性。本文针对上述问题,从蛋白质相互作用网络的拓扑结构出发,以图和复杂网络理论为基本工具,提出了四种有效的计算方法来对蛋白质相互作用数据中假阳性数据进行评估,并预测其假阴性数据和遗传相互作用。最后,我们提出一种在整合蛋白质相互作用数据、高内涵RNAi筛选数据和其它多源数据的基础上重建果蝇的MAPK信号转导通路的方法,以此作为蛋白质相互作用数据的一个应用实例。全文的主要工作概括如下：
(1)针对蛋白质相互作用数据中存在着较高比例假阳性数据的问题。提出了一种通过整合与蛋白质相互作用相关的多源异构组学数据,并巧妙地将多源数据信息与蛋白质相互作用网络的拓扑结构信息进行融合,进而过滤蛋白质相互作用数据中的假阳性“噪声”的方法。实验结果表明,所提出过滤算法的性能要优于已有的三种经典方法,能够筛选出原始数据中具有高度可靠性的蛋白质相互作用对。
(2)提出了一种鲁棒的基于流形学习ISOMAP的蛋白质相互作用假阳性过滤和假阴性预测的方法。该方法首先采用ISOMAP方法将原始的蛋白质相互作用网络变换到一个低维的流形空间。然后,根据所嵌入低维空间中蛋白质间的相似性构造了一个用来表示蛋白质对相互作用可能性的可靠性指数。实验结果显示,所提出的方法能够成功地评估或预测稠密或者稀疏蛋白质相互作用网络的假阳性或假阴性“噪声”。
(3)提出了一种新的基于线图和加权网络拓扑结构的方法来消除大规模蛋白质相互作用数据中的假阳性“噪声”。首先,采用一种新颖的加权线图算法将原始的蛋白质相互作用网络变换成其对应的加权线图；然后,计算变换后的加权线图中节点的多种网络拓扑属性。最后,采用一种加权的CD-Dist算法对蛋白质相互作用数据的可靠性进行了评估。实验结果表明,所提出的方法能够取得很好的去噪效果,过滤后的蛋白质相互作用数据可靠性得到了显著的提高。
(4)针对目前在基因组范围内的蛋白质遗传相互作用尚不完全了解,且通过实验的方法检测蛋白质遗传相互作用将非常困难和昂贵这一问题,提出了一种计算系统生物学方法来准确预测合成遗传相互作用。该方法首先通过整合蛋白质相互作用数据、蛋白质复合物数据和基因表达谱数据,构建一个高覆盖率、高精度的功能基因网络。然后,从上述功能基因网络中计算得到十种加权网络拓扑属性作为预测合成遗传相互作用的特征向量。最后,一种基于图的半监督分类器被用来预测合成遗传相互作用。实验结果表明,所提出的方法能够准确地预测酵母的遗传相互作用。
(5)提出了一种将RNA干扰技术、荧光显微镜技术和自动图像分析技术的结合的系统生物学方法来研究果蝇细胞的MAPK信号转导通路。该方法首先通过整合高内涵RNAi筛选数据、多源基因组学和蛋白质组学数据构建一个高可靠性的功能基因网络。然后,采用提出的一种改进的整数线性规划算法从所构建的功能基因网络中重建出果蝇MAPK信号通路。最后,通过p值、基因功能富集分析和已发表文献知识这三个指标来对得到的信号通路的生物显著性进行了验证。实验结果表明,所提出的方法不但能够发现KEGG标准数据库中存放的MAPK信号通路中包括的所有元素,而且还预测了一些额外的参与MAPK信号通路的蛋白质,通过文献查询,这些预测的蛋白质确实参与了MAPK信号通路。
Protein-protein interactions (PPI) play a very important role in almost every cellular processes.With the rapid advances in high-throughput experimental, biological experimental methods can directly and systematically detect protein interactions at the whole genome level for many organisms. In addition to the direct experimental data, a number of computational approaches have been proposed to predict the sets of interacting protein pairs. Unfortunately, current protein interactions detection via high-throughput experimental methods or prediction by computational methods are reported to exhibit high false positive and false negative "noises". At the same time, the false negative rate of the interaction networks has also been estimated to be high. In this dissertation, we propose a couple of computational algorithms to assess the reliability of interactions from the noisy data and then predict new interactions.
The purpose of this study was to investigate protein interaction networks from the topological aspect, and to develop four effective computational methods to automatically purify these networks, i.e., to detect false positive interactions from the existing protein interaction networks and discover unknown false negative interactions by their topological properties. Finally, we presented a novel application of using PPI networks to reconstruct signaling pathway. The main works and contributions for this dissertation are introduced as follows.
(1) The high-throughput experimental protein interaction data is prone to exhibit high level of false positive rates. A novel and effective approach was proposed to deal with this issue by integrating heterogeneous types of high-throughput biological data with weighted network topological metrics. We evaluate our proposed method on the Gavin's yeast interaction dataset. The experimental results show that by incorporating heterogeneous data types with weighted network topological metrics, our proposed method can improve functional homogeneity and localization coherence compared with those existing approaches.
(2) A robust manifold embedding technique was developed for assessing the reliability of interactions and predicting new interactions, which purely utilizes the topological information of PPI networks and can work on a sparse input protein interactome without requiring additional information types. After transforming a given PPI network into a low-dimensional metric space using manifold embedding based on isometric feature mapping (ISOMAP), the problem of assessing and predicting protein interactions can be recasted into the form of measuring similarity between the points located in its metric space. Then a reliability index, a likelihood indicating the interaction of two proteins, was assigned to each protein pair in the PPI networks based on the similarity between the points in the embedded space. Validation of the proposed method was performed with extensive experiments on densely connected and sparse PPI network of yeast, respectively. The results demonstrate that the interactions ranked top by our proposed method have high-functional homogeneity and localization coherence. Particularly, our method is very efficient for large sparse PPI network with which the traditional algorithms fail. Therefore, the proposed algorithm is a much more promising method for detecting both false positive and false negative interactions in PPI networks.
(3) A novel algorithm was proposed based on combination of line graph with weighted network toplogical metrics for eliminating false positive interactions from a PPI networks. A novel weighted line graph transformation method was firstly utilized to transform a PPI networks into line graph. Then, a number of network topological properties were computed. In order to define the similarity of proteins in the PPI network, a weighted Czekanowski-Kice distance metric was calculated on the basis of the obtained weighted PPI networks. Finally, the metrics wer used to assess the reliability of PPI data. The experimental results demonstate that by removing false positive protein interactions from the S.cerevisiae PPI networks, the reliability of the PPI dataset was significantly increased.
(4) A computational systems biology approach was introduced for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) was constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data. Then, a graph-based semisupervised learning (SSL) classifier was utilized to identify SGI, where the topological properties of protein pairs in weighted FGN was used as input features of the classifier. We compared the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in S. cerevisiae to validate the ability of our method to distinguish synthetic genetic interactions from non-interaction gene pairs. The experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae.
(5) A systems biology method was introduced to study the Drosophila Melanogaster MAPK signaling pathways by combining RNA interference (RNAi) technology, Fluorescence microscopy with automated image analysis. A high-quality functional gene network (FGN) was firstly derived by integrating high-content screen HCS and heterogeneous genomic data using a linear SVM classifier. Then the FGN was analyzed and the MAPK pathway was extracted using an extended integer linear programming. We validate our results and demonstrate that the proposed method achieves full coverage of components deposited in KEGG for the MAPK pathway. Interestingly, we retrieved a set of additional candidate genes for this pathway which are consistent with those published literatures.
(1)针对蛋白质相互作用数据中存在着较高比例假阳性数据的问题。提出了一种通过整合与蛋白质相互作用相关的多源异构组学数据,并巧妙地将多源数据信息与蛋白质相互作用网络的拓扑结构信息进行融合,进而过滤蛋白质相互作用数据中的假阳性“噪声”的方法。实验结果表明,所提出过滤算法的性能要优于已有的三种经典方法,能够筛选出原始数据中具有高度可靠性的蛋白质相互作用对。
(2)提出了一种鲁棒的基于流形学习ISOMAP的蛋白质相互作用假阳性过滤和假阴性预测的方法。该方法首先采用ISOMAP方法将原始的蛋白质相互作用网络变换到一个低维的流形空间。然后,根据所嵌入低维空间中蛋白质间的相似性构造了一个用来表示蛋白质对相互作用可能性的可靠性指数。实验结果显示,所提出的方法能够成功地评估或预测稠密或者稀疏蛋白质相互作用网络的假阳性或假阴性“噪声”。
(3)提出了一种新的基于线图和加权网络拓扑结构的方法来消除大规模蛋白质相互作用数据中的假阳性“噪声”。首先,采用一种新颖的加权线图算法将原始的蛋白质相互作用网络变换成其对应的加权线图；然后,计算变换后的加权线图中节点的多种网络拓扑属性。最后,采用一种加权的CD-Dist算法对蛋白质相互作用数据的可靠性进行了评估。实验结果表明,所提出的方法能够取得很好的去噪效果,过滤后的蛋白质相互作用数据可靠性得到了显著的提高。
(4)针对目前在基因组范围内的蛋白质遗传相互作用尚不完全了解,且通过实验的方法检测蛋白质遗传相互作用将非常困难和昂贵这一问题,提出了一种计算系统生物学方法来准确预测合成遗传相互作用。该方法首先通过整合蛋白质相互作用数据、蛋白质复合物数据和基因表达谱数据,构建一个高覆盖率、高精度的功能基因网络。然后,从上述功能基因网络中计算得到十种加权网络拓扑属性作为预测合成遗传相互作用的特征向量。最后,一种基于图的半监督分类器被用来预测合成遗传相互作用。实验结果表明,所提出的方法能够准确地预测酵母的遗传相互作用。
(5)提出了一种将RNA干扰技术、荧光显微镜技术和自动图像分析技术的结合的系统生物学方法来研究果蝇细胞的MAPK信号转导通路。该方法首先通过整合高内涵RNAi筛选数据、多源基因组学和蛋白质组学数据构建一个高可靠性的功能基因网络。然后,采用提出的一种改进的整数线性规划算法从所构建的功能基因网络中重建出果蝇MAPK信号通路。最后,通过p值、基因功能富集分析和已发表文献知识这三个指标来对得到的信号通路的生物显著性进行了验证。实验结果表明,所提出的方法不但能够发现KEGG标准数据库中存放的MAPK信号通路中包括的所有元素,而且还预测了一些额外的参与MAPK信号通路的蛋白质,通过文献查询,这些预测的蛋白质确实参与了MAPK信号通路。
Protein-protein interactions (PPI) play a very important role in almost every cellular processes.With the rapid advances in high-throughput experimental, biological experimental methods can directly and systematically detect protein interactions at the whole genome level for many organisms. In addition to the direct experimental data, a number of computational approaches have been proposed to predict the sets of interacting protein pairs. Unfortunately, current protein interactions detection via high-throughput experimental methods or prediction by computational methods are reported to exhibit high false positive and false negative "noises". At the same time, the false negative rate of the interaction networks has also been estimated to be high. In this dissertation, we propose a couple of computational algorithms to assess the reliability of interactions from the noisy data and then predict new interactions.
The purpose of this study was to investigate protein interaction networks from the topological aspect, and to develop four effective computational methods to automatically purify these networks, i.e., to detect false positive interactions from the existing protein interaction networks and discover unknown false negative interactions by their topological properties. Finally, we presented a novel application of using PPI networks to reconstruct signaling pathway. The main works and contributions for this dissertation are introduced as follows.
(1) The high-throughput experimental protein interaction data is prone to exhibit high level of false positive rates. A novel and effective approach was proposed to deal with this issue by integrating heterogeneous types of high-throughput biological data with weighted network topological metrics. We evaluate our proposed method on the Gavin's yeast interaction dataset. The experimental results show that by incorporating heterogeneous data types with weighted network topological metrics, our proposed method can improve functional homogeneity and localization coherence compared with those existing approaches.
(2) A robust manifold embedding technique was developed for assessing the reliability of interactions and predicting new interactions, which purely utilizes the topological information of PPI networks and can work on a sparse input protein interactome without requiring additional information types. After transforming a given PPI network into a low-dimensional metric space using manifold embedding based on isometric feature mapping (ISOMAP), the problem of assessing and predicting protein interactions can be recasted into the form of measuring similarity between the points located in its metric space. Then a reliability index, a likelihood indicating the interaction of two proteins, was assigned to each protein pair in the PPI networks based on the similarity between the points in the embedded space. Validation of the proposed method was performed with extensive experiments on densely connected and sparse PPI network of yeast, respectively. The results demonstrate that the interactions ranked top by our proposed method have high-functional homogeneity and localization coherence. Particularly, our method is very efficient for large sparse PPI network with which the traditional algorithms fail. Therefore, the proposed algorithm is a much more promising method for detecting both false positive and false negative interactions in PPI networks.
(3) A novel algorithm was proposed based on combination of line graph with weighted network toplogical metrics for eliminating false positive interactions from a PPI networks. A novel weighted line graph transformation method was firstly utilized to transform a PPI networks into line graph. Then, a number of network topological properties were computed. In order to define the similarity of proteins in the PPI network, a weighted Czekanowski-Kice distance metric was calculated on the basis of the obtained weighted PPI networks. Finally, the metrics wer used to assess the reliability of PPI data. The experimental results demonstate that by removing false positive protein interactions from the S.cerevisiae PPI networks, the reliability of the PPI dataset was significantly increased.
(4) A computational systems biology approach was introduced for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) was constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data. Then, a graph-based semisupervised learning (SSL) classifier was utilized to identify SGI, where the topological properties of protein pairs in weighted FGN was used as input features of the classifier. We compared the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in S. cerevisiae to validate the ability of our method to distinguish synthetic genetic interactions from non-interaction gene pairs. The experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae.
(5) A systems biology method was introduced to study the Drosophila Melanogaster MAPK signaling pathways by combining RNA interference (RNAi) technology, Fluorescence microscopy with automated image analysis. A high-quality functional gene network (FGN) was firstly derived by integrating high-content screen HCS and heterogeneous genomic data using a linear SVM classifier. Then the FGN was analyzed and the MAPK pathway was extracted using an extended integer linear programming. We validate our results and demonstrate that the proposed method achieves full coverage of components deposited in KEGG for the MAPK pathway. Interestingly, we retrieved a set of additional candidate genes for this pathway which are consistent with those published literatures.
引文
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