Evolving proteins at Darwin's bicentenary

A report of the Biochemical Society/Wellcome Trust meeting 'Protein Evolution - Sequences, Structures and Systems', Hinxton, UK, 26-27 January 2009

JNets: Exploring networks by integrating annotation

<p>Abstract</p> <p>Background</p> <p>A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.</p> <p>Results</p> <p>JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response.</p> <p>Conclusion</p> <p>JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at <url>http://www.manchester.ac.uk/bioinformatics/jnets</url>.</p

JNets: Exploring networks by integrating annotation

<p>Abstract</p> <p>Background</p> <p>A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.</p> <p>Results</p> <p>JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response.</p> <p>Conclusion</p> <p>JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at <url>http://www.manchester.ac.uk/bioinformatics/jnets</url>.</p

The biological context of HIV-1 host interactions reveals subtle insights into a system hijack

<p>Abstract</p> <p>Background</p> <p>In order to replicate, HIV, like all viruses, needs to invade a host cell and hijack it for its own use, a process that involves multiple protein interactions between virus and host. The HIV-1, Human Protein Interaction Database available at NCBI's website captures this information from the primary literature, containing over 2,500 unique interactions. We investigate the general properties and biological context of these interactions and, thus, explore the molecular specificity of the HIV-host perturbation. In particular, we investigate (i) whether HIV preferentially interacts with highly connected and 'central' proteins, (ii) known phenotypic properties of host proteins inferred from essentiality and disease-association data, and (iii) biological context (molecular function, processes and location) of the host proteins to identify attributes most strongly associated with specific HIV interactions.</p> <p>Results</p> <p>After correcting for ascertainment bias in the literature, we demonstrate a significantly greater propensity for HIV to interact with highly connected and central host proteins. Unexpectedly, we find there are no associations between HIV interaction and inferred essentiality. Similarly, we find a tendency for HIV not to interact with proteins encoded by genes associated with disease. Crucially, we find that functional categories over-represented in HIV-host interactions are innately enriched for highly connected and central proteins in the host system.</p> <p>Conclusions</p> <p>Our results imply that HIV's propensity to interact with highly connected and central proteins is a consequence of interactions with particular cellular functions, rather than being a direct effect of network topological properties. The lack of a propensity for interactions with phenotypically essential proteins suggests a selective pressure to minimise virulence in retroviral evolution. Thus, the specificity of HIV-host interactions is complex, and only superficially explained by network properties.</p

metaSHARK: a WWW platform for interactive exploration of metabolic networks

The metaSHARK (metabolic search and reconstruction kit) web server offers users an intuitive, fully interactive way to explore the KEGG metabolic network via a WWW browser. Metabolic reconstruction information for specific organisms, produced by our automated SHARKhunt tool or from other programs or genome annotations, may be uploaded to the website and overlaid on the generic network. Additional data from gene expression experiments can also be incorporated, allowing the visualization of differential gene expression in the context of the predicted metabolic network. metaSHARK is available at

Methodology capture: discriminating between the "best" and the rest of community practice

<p>Abstract</p> <p>Background</p> <p>The methodologies we use both enable and help define our research. However, as experimental complexity has increased the choice of appropriate methodologies has become an increasingly difficult task. This makes it difficult to keep track of available bioinformatics software, let alone the most suitable protocols in a specific research area. To remedy this we present an approach for capturing methodology from literature in order to identify and, thus, define best practice within a field.</p> <p>Results</p> <p>Our approach is to implement data extraction techniques on the full-text of scientific articles to obtain the set of experimental protocols used by an entire scientific discipline, molecular phylogenetics. Our methodology for identifying methodologies could in principle be applied to any scientific discipline, whether or not computer-based. We find a number of issues related to the nature of best practice, as opposed to community practice. We find that there is much heterogeneity in the use of molecular phylogenetic methods and software, some of which is related to poor specification of protocols. We also find that phylogenetic practice exhibits field-specific tendencies that have increased through time, despite the generic nature of the available software. We used the practice of highly published and widely collaborative researchers ("expert" researchers) to analyse the influence of authority on community practice. We find expert authors exhibit patterns of practice common to their field and therefore act as useful field-specific practice indicators.</p> <p>Conclusion</p> <p>We have identified a structured community of phylogenetic researchers performing analyses that are customary in their own local community and significantly different from those in other areas. Best practice information can help to bridge such subtle differences by increasing communication of protocols to a wider audience. We propose that the practice of expert authors from the field of evolutionary biology is the closest to contemporary best practice in phylogenetic experimental design. Capturing best practice is, however, a complex task and should also acknowledge the differences between fields such as the specific context of the analysis.</p

An exploration of alternative visualisations of the basic helix-loop-helix protein interaction network

Background: Alternative representations of biochemical networks emphasise different aspects of the data and contribute to the understanding of complex biological systems. In this study we present a variety of automated methods for visualisation of a protein-protein interaction network, using the basic helix-loop-helix ( bHLH) family of transcription factors as an example. Results: Network representations that arrange nodes ( proteins) according to either continuous or discrete information are investigated, revealing the existence of protein sub-families and the retention of interactions following gene duplication events. Methods of network visualisation in conjunction with a phylogenetic tree are presented, highlighting the evolutionary relationships between proteins, and clarifying the context of network hubs and interaction clusters. Finally, an optimisation technique is used to create a three-dimensional layout of the phylogenetic tree upon which the protein-protein interactions may be projected. Conclusion: We show that by incorporating secondary genomic, functional or phylogenetic information into network visualisation, it is possible to move beyond simple layout algorithms based on network topology towards more biologically meaningful representations. These new visualisations can give structure to complex networks and will greatly help in interpreting their evolutionary origins and functional implications. Three open source software packages (InterView, TVi and OptiMage) implementing our methods are available

All duplicates are not equal: the difference between small-scale and genome duplication

The comparison of pairs of gene duplications generated by small-scale duplications with those created by large-scale duplications shows that they differ in quantifiable ways. It is suggested that this is directly due to biases on the paths to gene retention rather than association with different functional categories

Bioinformatic analysis of the neprilysin (M13) family of peptidases reveals complex evolutionary and functional relationships

<p>Abstract</p> <p>Background</p> <p>The neprilysin (M13) family of endopeptidases are zinc-metalloenzymes, the majority of which are type II integral membrane proteins. The best characterised of this family is neprilysin, which has important roles in inactivating signalling peptides involved in modulating neuronal activity, blood pressure and the immune system. Other family members include the endothelin converting enzymes (ECE-1 and ECE-2), which are responsible for the final step in the synthesis of potent vasoconstrictor endothelins. The ECEs, as well as neprilysin, are considered valuable therapeutic targets for treating cardiovascular disease. Other members of the M13 family have not been functionally characterised, but are also likely to have biological roles regulating peptide signalling. The recent sequencing of animal genomes has greatly increased the number of M13 family members in protein databases, information which can be used to reveal evolutionary relationships and to gain insight into conserved biological roles.</p> <p>Results</p> <p>The phylogenetic analysis successfully resolved vertebrate M13 peptidases into seven classes, one of which appears to be specific to mammals, and insect genes into five functional classes and a series of expansions, which may include inactive peptidases. Nematode genes primarily resolved into groups containing no other taxa, bar the two nematode genes associated with <it>Drosophila </it>DmeNEP1 and DmeNEP4. This analysis reconstructed only one relationship between chordate and invertebrate clusters, that of the ECE sub-group and the DmeNEP3 related genes. Analysis of amino acid utilisation in the active site of M13 peptidases reveals a basis for their biochemical properties. A relatively invariant S1' subsite gives the majority of M13 peptidases their strong preference for hydrophobic residues in P1' position. The greater variation in the S2' subsite may be instrumental in determining the specificity of M13 peptidases for their substrates and thus allows M13 peptidases to fulfil a broad range of physiological roles.</p> <p>Conclusion</p> <p>The M13 family of peptidases have diversified extensively in all species examined, indicating wide ranging roles in numerous physiological processes. It is predicted that differences in the S2' subsite are fundamental to determining the substrate specificities that facilitate this functional diversity.</p

PathwayBooster:a tool to support the curation of metabolic pathways

BACKGROUND: Despite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task. RESULTS: We present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism’s metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms. CONCLUSIONS: By integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0447-2) contains supplementary material, which is available to authorized users