Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection

<p>Abstract</p> <p>Background</p> <p>Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease.</p> <p>Results</p> <p>We have predicted the protein interaction network of <it>Leishmania major </it>by using three validated methods: PSIMAP, PEIMAP, and iPfam. Combining the results from these methods, we calculated a high confidence network (confidence score > 0.70) with 1,366 nodes and 33,861 interactions. We were able to predict the biological process for 263 interacting proteins by doing enrichment analysis of the clusters detected. Analyzing the topology of the network with metrics such as connectivity and betweenness centrality, we detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets.</p> <p>Conclusion</p> <p>We have constructed the first protein interaction network of the <it>Leishmania major </it>parasite by using a computational approach. The topological analysis of the protein network enabled us to identify a set of candidate proteins that may be both (1) essential for parasite survival and (2) without human orthologs. These potential targets are promising for further experimental validation. This strategy, if validated, may augment established drug discovery methodologies, for this and possibly other tropical diseases, with a relatively low additional investment of time and resources.</p

AtPID: Arabidopsis thaliana protein interactome database—an integrative platform for plant systems biology

Arabidopsis thaliana Protein Interactome Database (AtPID) is an object database that integrates data from several bioinformatics prediction methods and manually collected information from the literature. It contains data relevant to protein–protein interaction, protein subcellular location, ortholog maps, domain attributes and gene regulation. The predicted protein interaction data were obtained from ortholog interactome, microarray profiles, GO annotation, and conserved domain and genome contexts. This database holds 28 062 protein–protein interaction pairs with 23 396 pairs generated from prediction methods. Among the rest 4666 pairs, 3866 pairs of them involving 1875 proteins were manually curated from the literature and 800 pairs were from enzyme complexes in KEGG. In addition, subcellular location information of 5562 proteins is available. AtPID was built via an intuitive query interface that provides easy access to the important features of proteins. Through the incorporation of both experimental and computational methods, AtPID is a rich source of information for system-level understanding of gene function and biological processes in A. thaliana. Public access to the AtPID database is available at http://atpid.biosino.org/

Gene expression and interactome analysis of candidate effectors associated with pre- and post-haustorial hemileia vastatrix-coffee interaction.

The present study sought to analyze the putative secreted proteins of Hemileia vastatrix, with potential to function as effector proteins. The H. vastatrix secretome was subjected to functional categorization and the greatest similarities were observed between species of the genus Puccinia sp (398), and Melampsora larici-populina (82). Based on the secretome, 415 Gene Ontology terms were extracted. The putative secretome was also compared to the high-throughput transcriptome of coffee-H. vastatrix interactions. By the transcriptome comparison data and the results of functional annotation and characteristics associated with effector proteins, 15 genes were selected and analyzed using RT-qPCR during compatible and incompatible coffee-H. vastatrix interactions. The expression patterns suggested that the EHv33-18 and EHv33-25 candidate effector may be responsible for faster communication between pathogen and the host during incompatible interaction. Other six candidate are involved in the biotrophic stage of infection, which is characterized by an increase in the expression of effectors, and in enzymes involved in secondary metabolism. Phylogenetic analysis suggest that these eight genes follow evolutionary mechanisms exclusive to the coffee-H. vastatrix interaction, making them important targets in studies aimed at obtaining durable resistance to this disease. Interactomic network made between coffee proteins and H. vastatrix proteins was obtained for the first time and revealed a wide network of interactions between effector EHv33-19 and coffee proteins. The obtained results suggest that there may be communication between the pathogen and the host in the early stage of infection during the urediniospores germination phase. This indicated pre-haustorial resistance complementary to post-haustorial resistance

Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis

BACKGROUND: Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks. METHODOLOGY/PRINCIPAL FINDINGS: Metabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the KEGG LIGAND database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways. CONCLUSIONS: We conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome based networks of organisms and the feasibility of their analyses through graph spectral methods. The insights obtained from such studies provide a broad overview of the similarities and differences between organisms, taking comparative genomics studies to a higher dimension

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Reconstructing and analysing protein-protein interaction networks of synaptic molecular machines

EPSRC Doctorate Training Centres (DTC) programme (EP/D505984/1)The postsynaptic density (PSD) is a complex, dynamic structure composed of ~2000 distinct proteins, found at the postsynaptic membrane. Interactions, of transient and non-transient nature, organise the PSD’s constituent parts into a protein complex, which functions as an intricately regulated molecular machine, orchestrating the mediation and regulation of synaptic transmission and synaptic plasticity. Furthermore, many of the proteins found in this complex have been linked to synaptic and behavioural plasticity, basic cognition or disease. Although, through proteomics we have accumulated a lot of information on the constituent parts of this machine as well smaller sub-networks representing pathways, not a lot is known about the organisational principles of the PSD. In this project our aim is to develop a standardised approach to reconstructing protein interaction networks from PSD proteomics data, providing a descriptive integrative model. Using these models we also performed an analysis elucidating parts of these organisational principles. We applied this method on two murine postsynaptic density proteomics datasets and found a persistent modular architecture of biological significance. Furthermore, given the lack of substantial evidence on the composition and architecture of postsynaptic density interaction networks of other model organisms, we decided to perform an affinity purification of Drosophila melanogaster postsynaptic density proteins and perform a similar analysis. The resulting model corroborated theoretical predictions of a lower complexity but similar functionality and also showed a modular architecture. As a final analysis we compared the two models from a structural and evolutionary perspective attempting to elucidate the mechanisms of evolution of this molecular machine. The results of this analysis implied that a whole component rather than just individual proteins of the fly protein interaction network have been conserved, highlighting the importance of the aforementioned organisational principles