Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.     

From biological pathways to regulatory networks

This paper presents a general theoretical framework for generating Boolean networks whose state transitions realize a set of given biological pathways or minor variations thereof. This ill-posed inverse problem, which is of crucial importance across practically all areas of biology, is solved by using Karnaugh maps which are classical tools for digital system design. It is shown that the incorporation of prior knowledge, presented in the form of biological pathways, can bring about a dramatic reduction in the cardinality of the network search space. Constraining the connectivity of the network, the number and relative importance of the attractors, and concordance with observed time-course data are additional factors that can be used to further reduce the cardinality of the search space. The networks produced by the approaches developed here should facilitate the understanding of multivariate biological phenomena and the subsequent design of intervention approaches that are more likely to be successful in practice. As an example, the results of this paper are applied to the widely studied p53 pathway and it is shown that the resulting network exhibits dynamic behavior consistent with experimental observations from the published literature.     

Homologous control of protein signaling networks

In a previous paper we introduced a method called augmented sparse reconstruction (ASR) that identifies links among nodes of ordinary differential equation networks, given a small set of observed trajectories with various initial conditions. The main purpose of that technique was to reconstruct intracellular protein signaling networks.In this paper we show that a recursive augmented sparse reconstruction generates artificial networks that are homologous to a large, reference network, in the sense that kinase inhibition of several reactions in the network alters the trajectories of a sizable number of proteins in comparable ways for reference and reconstructed networks. We show this result using a large in-silico model of the epidermal growth factor receptor (EGF-R) driven signaling cascade to generate the data used in the reconstruction algorithm.The most significant consequence of this observed homology is that a nearly optimal combinatorial dosage of kinase inhibitors can be inferred, for many nodes, from the reconstructed network, a result potentially useful for a variety of applications in personalized medicine.     

From pull-down data to protein interaction networks and complexes with biological relevance

Motivation: Recent improvements in high-throughput Mass Spectrometry(MS) technology have expedited genome-wide discovery of protein–proteininteractions by providing a capability of detecting proteincomplexes in a physiological setting. Computational inferenceof protein interaction networks and protein complexes from MSdata are challenging. Advances are required in developing robustand seamlessly integrated procedures for assessment of protein–proteininteraction affinities, mathematical representation of proteininteraction networks, discovery of protein complexes and evaluationof their biological relevance. Results: A multi-step but easy-to-follow framework for identifyingprotein complexes from MS pull-down data is introduced. It assessesinteraction affinity between two proteins based on similarityof their co-purification patterns derived from MS data. It constructsa protein interaction network by adopting a knowledge-guidedthreshold selection method. Based on the network, it identifiesprotein complexes and infers their core components using a graph-theoreticalapproach. It deploys a statistical evaluation procedure to assessbiological relevance of each found complex. On Saccharomycescerevisiae pull-down data, the framework outperformed othermore complicated schemes by at least 10% in F₁-measure and identified610 protein complexes with high-functional homogeneity basedon the enrichment in Gene Ontology (GO) annotation. Manual examinationof the complexes brought forward the hypotheses on cause offalse identifications. Namely, co-purification of differentprotein complexes as mediated by a common non-protein molecule,such as DNA, might be a source of false positives. Protein identificationbias in pull-down technology, such as the hydrophilic bias couldresult in false negatives. Contact: samatovan{at}ornl.gov Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Jonathan Wren Present address: Department of Biomedical Informatics, VanderbiltUniversity, Nashville, TN 37232. The authors wish it to be known that, in their opinion, thefirst two authors should be regarded as joint First Authors.     

A common neighbor based technique to detect protein complexes in PPI networks

Detection of protein complexes by analyzing and understanding PPI networks is an important task and critical to all aspects of cell biology. We present a technique called PROtein COmplex DEtection based on common neighborhood (PROCODE) that considers the inherent organization of protein complexes as well as the regions with heavy interactions in PPI networks to detect protein complexes. Initially, the core of the protein complexes is detected based on the neighborhood of PPI network. Then a merging strategy based on density is used to attach proteins and protein complexes to the core-protein complexes to form biologically meaningful structures. The predicted protein complexes of PROCODE was evaluated and analyzed using four PPI network datasets out of which three were from budding yeast and one from human. Our proposed technique is compared with some of the existing techniques using standard benchmark complexes and PROCODE was found to match very well with actual protein complexes in the benchmark data. The detected complexes were at par with existing biological evidence and knowledge.     

Plant secretome — From cellular process to biological activity

Recent studies suggest that plants secrete a large number of proteins and peptides into the extracellular space. Secreted proteins play a crucial role in stress response, communication and development of organisms. Here we review the current knowledge of the secretome of more than ten plant species, studied in natural conditions or during (a)biotic stress. This review not only deals with the classical secretory route via endoplasmic reticulum and Golgi followed by proteins containing a known N-terminal signal peptide, but also covers new findings about unconventional secretion of leaderless proteins. We describe alternative secretion pathways and the involved compartments like the recently discovered EXPO. The well characterized secreted peptides that function as ligands of receptor proteins exemplify the biological significance and activity of the secretome. This article is part of a Special Issue entitled: An Updated Secretome.     

Modelling biological systems from molecules to dynamical networks

A report of the 5^th IEEE International Conference on Systems Biology (IEEE ISB2011), 2-4 September 2011, Zhuhai, China.     

Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm

MOTIVATION: Predicting protein function accurately is an important issue in the post-genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co-expression profiles, and other information. Among these methods, the global optimization method (GOM) is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interactions network [Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. (2003) Global protein function prediction from protein-protein interaction networks, Nat. Biotechnol., 21, 697-700]. To boost both the accuracy and speed of GOM, a new prediction method, MFGO (modified and faster global optimization) is presented in this paper, which employs local optimal repetition method to reduce calculation time, and takes account of topological structure information to achieve a more accurate prediction. CONCLUSION: On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP-core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO's improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment.     

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex–complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery‐potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.     

Prediction of regulation relationship between protein interactions in signaling networks

The discovery of regulation relationship of protein interactions is crucial for the mechanism research in signaling network. Bioinformatics methods can be used to accelerate the discovery of regulation relationship between protein interactions, to distinguish the activation relations from inhibition relations. In this paper, we describe a novel method to predict the regulation relations of protein interactions in the signaling network. We detected 4,417 domain pairs that were significantly enriched in the activation or inhibition dataset. Three machine learning methods, logistic regression, support vector machines(SVMs), and naïve bayes, were explored in the classifier models. The prediction power of three different models was evaluated by 5-fold cross-validation and the independent test dataset. The area under the receiver operating characteristic curve for logistic regression, SVM, and naïve bayes models was 0.946, 0.905 and 0.809, respectively. Finally, the logistic regression classifier was applied to the human proteome-wide interaction dataset, and 2,591 interactions were predicted with their regulation relations, with 2,048 in activation and 543 in inhibition. This model based on domains can be used to identify the regulation relations between protein interactions and furthermore reconstruct signaling pathways.     

Reactome pathway analysis to enrich biological discovery in proteomics data sets

Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).     

From protein interaction networks to novel therapeutic strategies

Cellular mechanisms that sustain health or contribute to disease emerge mostly from the complex interplay among various molecular entities. To understand the underlying relationships between genotype, environment and phenotype, one has to consider the intricate and nonsequential interaction patterns formed between the different sets of cellular players. Biological networks capture a variety of molecular interactions and thus provide an excellent opportunity to consider physiological characteristics of individual molecules within their cellular context. In particular, the concept of network biology and its applications contributed largely to recent advances in biomedical research. In this review, we show (i) how biological networks, i.e., protein-protein interaction networks, facilitate the understanding of pathogenic mechanisms that trigger the onset and progression of diseases and (ii) how this knowledge can be translated into effective diagnostic and therapeutic strategies. In particular, we focus on the impact of network pharmacological concepts that go beyond the classical view on individual drugs and targets aiming for combinational therapies with improved clinical efficacy and reduced safety risks.     

Dynamical systems for discovering protein complexes and functional modules from biological networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the heaviest k-subgraph problem (k-HSP), which itself is NP-hard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a "spurious" heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the standard deviation and mean ratio (SMR), is proposed for use in "spurious" heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal     

Characterization of the EGFR interactome reveals associated protein complex networks and intracellular receptor dynamics

Growth factor receptor mediated signaling is meanwhile recognized as a complex signaling network, which is initiated by recruiting specific patterns of adaptor proteins to the intracellular domain of epidermal growth factor receptor (EGFR). Approaches to globally identify EGFR‐binding proteins are required to elucidate this network. We affinity‐purified EGFR with its interacting proteins by coprecipitation from lysates of A431 cells. A total of 183 proteins were repeatedly detected in high‐resolution MS measurements. For 15 of these, direct interactions with EGFR were listed in the iRefIndex interaction database, including Grb2, shc‐1, SOS1 and 2, STAT 1 and 3, AP2, UBS3B, and ERRFI. The newly developed Cytoscape plugin ModuleGraph allowed retrieving and visualizing 93 well‐described protein complexes that contained at least one of the proteins found to interact with EGFR in our experiments. Abundances of 14 proteins were modulated more than twofold upon EGFR activation whereof clathrin‐associated adaptor complex AP‐2 showed 4.6‐fold enrichment. These proteins were further annotated with different cellular compartments. Finally, interactions of AP‐2 proteins and the newly discovered interaction of CIP2A could be verified. In conclusion, a powerful technique is presented that allowed identification and quantitative assessment of the EGFR interactome to provide further insight into EGFR signaling.     

Multi-faceted approaches toward unravelling complex ecological networks

The main theme of this Special Feature is the complexity-stability relationship and diversity of interaction types. Five articles by leading authors are submitted. Studies on the relationship between complexity and stability have a long history of 40 years. Effect of multiple interaction types on structure and dynamics of an ecological network is a recent important subject to be resolved. In this preface, I briefly review the history of research on complexity-stability relationship till the idea of diversity of interaction types appears.     

DnaA protein DNA-binding domain binds to Hda protein to promote inter-AAA+ domain interaction involved in regulatory inactivation of DnaA

Chromosomal replication is initiated from the replication origin oriC in Escherichia coli by the active ATP-bound form of DnaA protein. The regulatory inactivation of DnaA (RIDA) system, a complex of the ADP-bound Hda and the DNA-loaded replicase clamp, represses extra initiations by facilitating DnaA-bound ATP hydrolysis, yielding the inactive ADP-bound form of DnaA. However, the mechanisms involved in promoting the DnaA-Hda interaction have not been determined except for the involvement of an interaction between the AAA+ domains of the two. This study revealed that DnaA Leu-422 and Pro-423 residues within DnaA domain IV, including a typical DNA-binding HTH motif, are specifically required for RIDA-dependent ATP hydrolysis in vitro and that these residues support efficient interaction with the DNA-loaded clamp·Hda complex and with Hda in vitro. Consistently, substitutions of these residues caused accumulation of ATP-bound DnaA in vivo and oriC-dependent inhibition of cell growth. Leu-422 plays a more important role in these activities than Pro-423. By contrast, neither of these residues is crucial for DNA replication from oriC, although they are highly conserved in DnaA orthologues. Structural analysis of a DnaA·Hda complex model suggested that these residues make contact with residues in the vicinity of the Hda AAA+ sensor I that participates in formation of a nucleotide-interacting surface. Together, the results show that functional DnaA-Hda interactions require a second interaction site within DnaA domain IV in addition to the AAA+ domain and suggest that these interactions are crucial for the formation of RIDA complexes that are active for DnaA-ATP hydrolysis.     

Neutral networks of sequence to shape maps

In this paper we present a combinatorial model of sequence to shape maps. Our particular construction arises in the context of representing nucleotide interactions beyond Watson-Crick base pairs and its key feature is to replace biophysical steric by combinatorial constraints. We show that these combinatory maps produce exponentially many shapes and induce sets of sequences which contain extended connected subgraphs of diameter n, where n denotes the length of the sequence. Our main result is to prove the existence of exponentially many shapes that have neutral networks.