Computational Methods for Identifying Conserved Protein Complexes between Species from Protein Interaction Data

Master'sMASTER OF SCIENC

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

Comprehensively Surveying Structure and Function of RING Domains from Drosophila melanogaster

Using a complete set of RING domains from Drosophila melanogaster, all the solved RING domains and cocrystal structures of RING-containing ubiquitin-ligases (RING-E3) and ubiquitin-conjugating enzyme (E2) pairs, we analyzed RING domains structures from their primary to quarternary structures. The results showed that: i) putative orthologs of RING domains between Drosophila melanogaster and the human largely occur (118/139, 84.9%); ii) of the 118 orthologous pairs from Drosophila melanogaster and the human, 117 pairs (117/118, 99.2%) were found to retain entirely uniform domain architectures, only Iap2/Diap2 experienced evolutionary expansion of domain architecture; iii) 4 evolutionary structurally conserved regions (SCRs) are responsible for homologous folding of RING domains at the superfamily level; iv) besides the conserved Cys/His chelating zinc ions, 6 equivalent residues (4 hydrophobic and 2 polar residues) in the SCRs possess good-consensus and conservation- these 4 SCRs function in the structural positioning of 6 equivalent residues as determinants for RING-E3 catalysis; v) members of these RING proteins located nucleus, multiple subcellular compartments, membrane protein and mitochondrion are respectively 42 (42/139, 30.2%), 71 (71/139, 51.1%), 22 (22/139, 15.8%) and 4 (4/139, 2.9%); vi) CG15104 (Topors) and CG1134 (Mul1) in C3HC4, and CG3929 (Deltex) in C3H2C3 seem to display broader E2s binding profiles than other RING-E3s; vii) analyzing intermolecular interfaces of E2/RING-E3 complexes indicate that residues directly interacting with E2s are all from the SCRs in RING domains. Of the 6 residues, 2 hydrophobic ones contribute to constructing the conserved hydrophobic core, while the 2 hydrophobic and 2 polar residues directly participate in E2/RING-E3 interactions. Based on sequence and structural data, SCRs, conserved equivalent residues and features of intermolecular interfaces were extracted, highlighting the presence of a nucleus for RING domain fold and formation of catalytic core in which related residues and regions exhibit preferential evolutionary conservation

A proteome-wide protein interaction map for Campylobacter jejuni

'Systematic identification of protein interactions for the bacterium Campylobacter jejuni using high-throughput yeast two-hybrid screens detected interactions for 80% of the organism's proteins

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

<p>Abstract</p> <p>Background</p> <p>Protein-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 <it>Magnaporthe grisea</it>, 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.</p> <p>Results</p> <p>Using a well-recognized interolog approach, we have predicted 11,674 interactions among 3,017 <it>M. grisea </it>proteins. Although the scale of the constructed map covers approximately only one-fourth of the <it>M. grisea</it>'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 <it>M. grisea </it>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.</p> <p>Conclusion</p> <p>We predicted the PPIs of <it>M. grisea </it>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 <url>http://bioinformatics.cau.edu.cn/zzd_lab/MPID.html</url>.</p

An Interaction Network Predicted from Public Data as a Discovery Tool: Application to the Hsp90 Molecular Chaperone Machine

Understanding the functions of proteins requires information about their protein-protein interactions (PPI). The collective effort of the scientific community generates far more data on any given protein than individual experimental approaches. The latter are often too limited to reveal an interactome comprehensively. We developed a workflow for parallel mining of all major PPI databases, containing data from several model organisms, and to integrate data from the literature for a protein of interest. We applied this novel approach to build the PPI network of the human Hsp90 molecular chaperone machine (Hsp90Int) for which previous efforts have yielded limited and poorly overlapping sets of interactors. We demonstrate the power of the Hsp90Int database as a discovery tool by validating the prediction that the Hsp90 co-chaperone Aha1 is involved in nucleocytoplasmic transport. Thus, we both describe how to build a custom database and introduce a powerful new resource for the scientific community

The Drosophila Interactions Database: Integrating The Interactome And Transcriptome

In this thesis I describe the integration of heterogeneous interaction data for Drosophila into DroID, the Drosophilainteractions database, making it a one-stop public resource for interaction data. I have also made it possible to filter the interaction data using gene expression data to generate context-relevant networks making DroID a one-of-a kind resource for biologists. In the two years since the upgraded DroID has been available, several studies have used the heterogeneous interaction data in DroID to advance our understanding of Drosophila biology thus validating the need for such a resource for biologists. In addition to this, I have identified organizing principles of interaction networks based on genome-wide gene expression data in the tissues and the entire life cycle of Drosophila. I have shown that all tissues and stages have a core ubiquitously expressed PPI network to which tissue and stage specific proteins attach to potentially modulate specific functions. In view of these organizing principles, I developed a normalized expression filter for interaction networks. I have shown that networks generated by using this filter are context-relevant as evidenced by their enrichment for genes with relevant mutant phenotypes. This filter has been implemented in DroID and I anticipate that studies on interactome networks using this filter will further our understanding of biology

A global analysis of genetic interactions in Caenorhabditis elegans

Abstract Background Understanding gene function and genetic relationships is fundamental to our efforts to better understand biological systems. Previous studies systematically describing genetic interactions on a global scale have either focused on core biological processes in protozoans or surveyed catastrophic interactions in metazoans. Here, we describe a reliable high-throughput approach capable of revealing both weak and strong genetic interactions in the nematode Caenorhabditis elegans. Results We investigated interactions between 11 'query' mutants in conserved signal transduction pathways and hundreds of 'target' genes compromised by RNA interference (RNAi). Mutant-RNAi combinations that grew more slowly than controls were identified, and genetic interactions inferred through an unbiased global analysis of the interaction matrix. A network of 1,246 interactions was uncovered, establishing the largest metazoan genetic-interaction network to date. We refer to this approach as systematic genetic interaction analysis (SGI). To investigate how genetic interactions connect genes on a global scale, we superimposed the SGI network on existing networks of physical, genetic, phenotypic and coexpression interactions. We identified 56 putative functional modules within the superimposed network, one of which regulates fat accumulation and is coordinated by interactions with bar-1(ga80), which encodes a homolog of β-catenin. We also discovered that SGI interactions link distinct subnetworks on a global scale. Finally, we showed that the properties of genetic networks are conserved between C. elegans and Saccharomyces cerevisiae, but that the connectivity of interactions within the current networks is not. Conclusions Synthetic genetic interactions may reveal redundancy among functional modules on a global scale, which is a previously unappreciated level of organization within metazoan systems. Although the buffering between functional modules may differ between species, studying these differences may provide insight into the evolution of divergent form and function

Computational prediction of host-pathogen protein-protein interactions

Philosophiae Doctor - PhDSupervised machine learning approaches have been applied successfully to the prediction of protein-protein interactions (PPIs) within a single organism, i.e., intra-species predictions. However, because of the absence of large amounts of experimentally validated PPIs data for training and testing, fewer studies have successfully applied these techniques to host-pathogen PPI, i.e., inter-species comparisons. Among the host-pathogen studies, most of them have focused on human-virus interactions and specifically human-HIV PPI data. Additional improvements to machine learning techniques and feature sets are important to improve the classification accuracy for host-pathogen protein-protein interactions prediction. The primary aim of this bioinformatics thesis was to develop a binary classifier with an appropriate feature set for host-pathogen protein-protein interaction prediction using published human-Hepatitis C virus PPI, and to test the model on available host-pathogen data for human-Bacillus anthracis PPI. Twelve different feature sets were compared to find the optimal set. The feature selection process reveals that our novel quadruple feature (a subsequence of four consecutive amino acid) combined with sequence similarity and human interactome network properties (such as degree, cluster coefficient, and betweenness centrality) were the best set. The optimal feature set outperformed those in the relevant published material, giving 95.9% sensitivity, 91.6% specificity and 89.0% accuracy. Using our optimal features set, we developed a neural network model to predict PPI between human-Mycobacterium tuberculosis. The strategy is to develop a model trained with intra-species PPI data and extend it to inter-species prediction. However, the lack of experimentally validated PPI data between human-Mycobacterium tuberculosis (Mtuberculosis), leads us to first assess the feasibility of using validated intra-species PPI data to build a model for inter-species PPI. In this model we used human intra-species PPI combined with Bacillus anthracis intra-species data to develop a binary classification model and extend the model for human-Bacillus anthracis inter-species prediction. Thus, we test our hypotheses on known human-Bacillus anthracis PPI data and the result shows good performance with 89.0% as average accuracy. The same approach was extended to the prediction of PPI between human-Mycobacterium tuberculosis. The predicted human-M-tuberculosis PPI data were further validated using functional enrichment of experimentally verified secretory proteins in M-tuberculosis, cellular compartment analysis and pathway enrichment analysis. Results show that five of the M-tuberculosis secretory proteins within an infected host macrophage that correspond to the mycobacterial virulent strain H37Rv were extracted from the human-M- tuberculosis PPI dataset predicted by our model. Finally, a web server was created to predict PPIs between human and Mycobacterium tuberculosis which is available online at URL:http://hppredict.sanbi.ac.za. In summary, the concepts, techniques and technologies developed as part of this thesis have the potential to contribute not only to the understanding PPI analysis between human and Mycobacterium tuberculosis, but can be extended to other pathogens. Further materials related to this study are available at ftp://ftp.sanbi.ac.za/machine learning.National Research Foundation (NRF) and SANB