Yeast Protein-protein Interaction Data Research Articles

Adaptive Large Neighborhood Search Enhances Global Network Alignment

Aligning protein-protein interaction networks from different species is a useful mechanism for figuring out orthologous proteins, predicting/verifying protein unknown functions or constructing evolutionary relationships. The network alignment problem is proved to be NP-hard, requiring exponential-time algorithms, which is not feasible for the fast growth of biological data. In this paper, we present a novel protein-protein interaction global network alignment algorithm, which is enhanced with an extended large neighborhood search heuristics. Evaluated on benchmark datasets of yeast, fly, human and worm, the proposed algorithm outperforms state-of-the-art. Furthermore, the complexity of ours is polynomial, thus being scalable to large biological networks in practice.
 Keywords
 Heuristic, Protein-protein interaction networks, network alignment, neighborhood search
 References
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A cell-core-attachment approach for identifying protein complexes in yeast protein-protein interaction network

With the increasing of available protein-protein interaction (PPI) data, many computational methods have been explored to identify protein complexes from PPI networks. Majority of algorithms employ the feature of local neighbors to detect local dense subgraphs which correspond to protein complexes. Those approaches neglect the inherent core-attachment structure of protein complexes, which to an extent affect the protein complexes of prediction accuracy. In this paper, we propose a new algorithm for predicting protein complexes, deriving from the framework of the core-attachment. The proposed method first obtains the triangular structures of the core of protein complexes, name as cells, in which the edge-clustering coefficient is used. And then the cells are expanded to protein complex cores based on the closeness. Finally, the attachments are added to their corresponding cores to form the final protein complexes. The experimental results on two yeast PPI data show our method outperform the existing algorithms in terms of matched protein complexes and biological significance using two benchmark data sets.

Predicting overlapping protein complexes in weighted interactome networks

Protein complexes play important roles in integrating individual gene products to perform useful cellular functions. The increasing mount of protein-protein interaction (PPI) data has enabled us to predict protein complexes. In spite of the advances in these computational approaches and experimental techniques, it is impossible to construct an absolutely reliable PPI network. Taking into account the reliability of interactions in the PPI network, we have constructed a weighted protein-protein interaction (WPPI) network, in which the reliability of each interaction is represented as a weight using the topology of the PPI network. As overlaps are likely to have biological importance, we proposed a novel method named WN-PC (weighted network-based method for predicting protein complexes) to predict overlapping protein complexes on the WPPI network. The proposed algorithm predicts neighborhood graphs with an aggregation coefficient over a threshold as candidate complexes, and binds attachment proteins to candidate complexes. Finally, we have filtered redundant complexes which overlap other complexes to a very high extent in comparison to their density and size. A comprehensive comparison between competitive algorithms and our WN-PC method has been made in terms of the F-measure, coverage rate, and P-value. We have applied WN-PC to two different yeast PPI data sets, one of which is a huge PPI network consisting of over 6000 proteins and 200 000 interactions. Experimental results show that WN-PC outperforms the state-of-the-art methods. We think that our research may be helpful for other applications in PPI networks.

Protein Complex Prediction in Large Ontology Attributed Protein-Protein Interaction Networks

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

Accuracy improvement in protein complex prediction from protein interaction networks by refining cluster overlaps

BackgroundRecent computational techniques have facilitated analyzing genome-wide protein-protein interaction data for several model organisms. Various graph-clustering algorithms have been applied to protein interaction networks on the genomic scale for predicting the entire set of potential protein complexes. In particular, the density-based clustering algorithms which are able to generate overlapping clusters, i.e. the clusters sharing a set of nodes, are well-suited to protein complex detection because each protein could be a member of multiple complexes. However, their accuracy is still limited because of complex overlap patterns of their output clusters.ResultsWe present a systematic approach of refining the overlapping clusters identified from protein interaction networks. We have designed novel metrics to assess cluster overlaps: overlap coverage and overlapping consistency. We then propose an overlap refinement algorithm. It takes as input the clusters produced by existing density-based graph-clustering methods and generates a set of refined clusters by parameterizing the metrics. To evaluate protein complex prediction accuracy, we used the f-measure by comparing each refined cluster to known protein complexes. The experimental results with the yeast protein-protein interaction data sets from BioGRID and DIP demonstrate that accuracy on protein complex prediction has increased significantly after refining cluster overlaps.ConclusionsThe effectiveness of the proposed cluster overlap refinement approach for protein complex detection has been validated in this study. Analyzing overlaps of the clusters from protein interaction networks is a crucial task for understanding of functional roles of proteins and topological characteristics of the functional systems.

A domain-based approach to predict protein-protein interactions.

BackgroundKnowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.ResultsDomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.ConclusionWe envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.

Discovering reliable protein interactions from high-throughput experimental data using network topology

Current protein-protein interaction (PPI) detection via high-throughput experimental methods, such as yeast-two-hybrid has been reported to be highly erroneous, leading to potentially costly spurious discoveries. This work introduces a novel measure called IRAP, i.e. "interaction reliability by alternative path", for assessing the reliability of protein interactions based on the underlying topology of the PPI network. A candidate PPI is considered to be reliable if it is involved in a closed loop in which the alternative path of interactions between the two interacting proteins is strong. We devise an algorithm called AlternativePathFinder to compute the IRAP value for each interaction in a complex PPI network. Validation of the IRAP as a measure for assessing the reliability of PPIs is performed with extensive experiments on yeast PPI data. All the data used in our experiments can be downloaded from our supplementary data web site at . Results show consistently that IRAP measure is an effective way for discovering reliable PPIs in large datasets of error-prone experimentally-derived PPIs. Results also indicate that IRAP is better than IG2, and markedly better than the more simplistic IG1 measure. Experimental results demonstrate that a global, system-wide approach-such as IRAP that considers the entire interaction network instead of merely local neighbors-is a much more promising approach for assessing the reliability of PPIs.