Protein-protein Interaction Network Prediction Research Articles

Evolution of protein-protein interaction networks in yeast.

Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information.

Structural details of protein interactions are invaluable to the understanding of cellular processes. However, the identification of interactions at atomic resolution is a continuing challenge in the systems biology era. Although the number of structurally resolved complexes in the Protein Databank increases exponentially, the complexes only cover a small portion of the known structural interactome. In this chapter, we review the PRISM system that is a protein-protein interaction (PPI) prediction tool-its rationale, principles, and applications. We further discuss its extensions to discover the effect of single residue mutations, to model large protein assemblies, to improve its performance by exploiting conformational protein ensembles, and to reconstruct large PPI networks or pathway maps.

Protein-protein interaction network prediction using stochastic learning automata induced differential evolution

Protein-protein interactions (PPIs) are of biological interest for their active participation in coordinating a number of cellular processes in living organisms. This paper attempts to formulate PPIs as an optimization problem with an aim to independently maximize (a) the stability of a complex formed by two proteins predicted to be interacting, (b) the difference between their individual accessible solvent area and that of the corresponding protein-protein complex, (c) their functional similarity and d) the occurrence of their interacting domain pairs. The novelty of the paper lies in ranking the set of PPI networks, obtained through independently optimizing individual objectives, using two approaches. The first approach is concerned with identifying the equally good PPI networks based on their fitness-based ranks with respect to individual four objectives. The second approach aims at sorting the PPI networks based on their fuzzy memberships to satisfy individual four objectives. The paper also proposes a novel single objective optimization algorithm to optimize individual objectives, influencing the true prediction of a PPI network. The proposed algorithm is realized by an amalgamation of the differential evolution and the stochastic learning automata, where the former is employed to globally explore the search space and the latter for the adaptive tuning of the control parameter of the algorithm. The proposed technique outperforms the existing methods, including relative specific similarity, domain cohesion coupling, random decision forest, fuzzy support vector machine and evolutionary/swarm algorithm based approaches, with respect to both sensitivity and specificity.

Integrative molecular profiling indicates a central role of transitory starch breakdown in establishing a stable C/N homeostasis during cold acclimation in two natural accessions of Arabidopsis thaliana.

BackgroundThe variation of growth and cold tolerance of two natural Arabidopsis accessions, Cvi (cold sensitive) and Rschew (cold tolerant), was analysed on a proteomic, phosphoproteomic and metabolomic level to derive characteristic information about genotypically distinct strategies of metabolic reprogramming and growth maintenance during cold acclimation.ResultsGrowth regulation before and after a cold acclimation period was monitored by recording fresh weight of leaf rosettes. Significant differences in the shoot fresh weight of Cvi and Rschew were detected both before and after acclimation to low temperature. During cold acclimation, starch levels were found to accumulate to a significantly higher level in Cvi compared to Rschew. Concomitantly, statistical analysis revealed a cold-induced decrease of beta-amylase 3 (BAM3; AT4G17090) in Cvi but not in Rschew. Further, only in Rschew we observed an increase of the protein level of the debranching enzyme isoamylase 3 (ISA3; AT4G09020). Additionally, the cold response of both accessions was observed to severely affect ribosomal complexes, but only Rschew showed a pronounced accumulation of carbon and nitrogen compounds. The abundance of the Cold Regulated (COR) protein COR78 (AT5G52310) as well as its phosphorylation was observed to be positively correlated with the acclimation state of both accessions. In addition, transcription factors being involved in growth and developmental regulation were found to characteristically separate the cold sensitive from the cold tolerant accession. Predicted protein-protein interaction networks (PPIN) of significantly changed proteins during cold acclimation allowed for a differentiation between both accessions. The PPIN revealed the central role of carbon/nitrogen allocation and ribosomal complex formation to establish a new cold-induced metabolic homeostasis as also observed on the level of the metabolome and proteome.ConclusionOur results provide evidence for a comprehensive multi-functional molecular interaction network orchestrating growth regulation and cold acclimation in two natural accessions of Arabidopsis thaliana. The differential abundance of beta-amylase 3 and isoamylase 3 indicates a central role of transitory starch degradation in the coordination of growth regulation and the development of stress tolerance. Finally, our study indicates naturally occurring differential patterns of C/N balance and protein synthesis during cold acclimation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0668-1) contains supplementary material, which is available to authorized users.

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis

Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational method of high-throughput PPI network prediction based on all-to-all rigid-body docking of protein tertiary structures. The prediction system accepts a set of data comprising protein tertiary structures as input and generates a list of possible interacting pairs from all the combinations as output. A crucial advantage of this docking based method is in providing predictions of protein pairs that increases our understanding of biological pathways by analyzing the structures of candidate complex structures, which gives insight into novel interaction mechanisms. Although such exhaustive docking calculation requires massive computational resources, recent advancements in the computational sciences have made such large-scale calculations feasible. different rigid-body docking tools with different scoring models. We found that the predicted interactions were different between the results from the two tools. When the positive predictions from both of the docking tools were combined, all the core signaling interactions were correctly predicted with the exception of interactions activated by protein phosphorylation. Large-scale PPI prediction using tertiary structures is an effective approach that has a wide range of potential applications. This method is especially useful for identifying novel PPIs of new pathways that control cellular behavior.

Prediction and characterization of protein-protein interaction networks in swine

BackgroundStudying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.ResultsWe used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.ConclusionThe results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

The prediction of protein-protein interaction networks in rice blast fungus

BackgroundProtein-protein interaction (PPI) maps are useful tools for investigating the cellular functions of genes. Thus far, large-scale PPI mapping projects have not been implemented for the rice blast fungus Magnaporthe grisea, which is responsible for the most severe rice disease. Inspired by recent advances in PPI prediction, we constructed a PPI map of this important fungus.ResultsUsing a well-recognized interolog approach, we have predicted 11,674 interactions among 3,017 M. grisea proteins. Although the scale of the constructed map covers approximately only one-fourth of the M. grisea's proteome, it is the first PPI map for this crucial organism and will therefore provide new insights into the functional genomics of the rice blast fungus. Focusing on the network topology of proteins encoded by known pathogenicity genes, we have found that pathogenicity proteins tend to interact with higher numbers of proteins. The pathogenicity proteins and their interacting partners in the entire network were then used to construct a subnet called a pathogenicity network. These data may provide further clues for the study of these pathogenicity proteins. Finally, it has been established that secreted proteins in M. grisea interact with fewer proteins. These secreted proteins and their interacting partners were also compiled into a network of secreted proteins, which may be helpful in constructing an interactome between the rice blast fungus and rice.ConclusionWe predicted the PPIs of M. grisea and compiled them into a database server called MPID. It is hoped that MPID will provide new hints as to the functional genomics of this fungus. MPID is available at .

Investigation of factors affecting prediction of protein-protein interaction networks by phylogenetic profiling

BackgroundThe use of computational methods for predicting protein interaction networks will continue to grow with the number of fully sequenced genomes available. The Co-Conservation method, also known as the Phylogenetic profiles method, is a well-established computational tool for predicting functional relationships between proteins.ResultsHere, we examined how various aspects of this method affect the accuracy and topology of protein interaction networks. We have shown that the choice of reference genome influences the number of predictions involving proteins of previously unknown function, the accuracy of predicted interactions, and the topology of predicted interaction networks. We show that while such results are relatively insensitive to the E-value threshold used in defining homologs, predicted interactions are influenced by the similarity metric that is employed. We show that differences in predicted protein interactions are biologically meaningful, where judicious selection of reference genomes, or use of a new scoring scheme that explicitly considers reference genome relatedness, produces known protein interactions as well as predicted protein interactions involving coordinated biological processes that are not accessible using currently available databases.ConclusionThese studies should prove valuable for future studies seeking to further improve phylogenetic profiling methodologies as well for efforts to efficiently employ such methods to develop new biological insights.