BACKGROUND: Network motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes. Network motifs may also become pivotal in the rational design and engineering of complex biological systems underpinning the field of synthetic biology. Distinguishing true motifs from arbitrary substructures, however, remains a challenge. RESULTS: Here we demonstrate both theoretically and empirically that implicit assumptions present in mainstream methods for motif identification do not necessarily hold, with the ramification that motif studies using these mainstream methods are less able to effectively differentiate between spurious results and events of true statistical significance than is often presented. We show that these difficulties cannot be overcome without revising the methods of statistical analysis used to identify motifs. CONCLUSIONS: Present-day methods for the discovery of network motifs, and, indeed, even the methods for defining what they are, are critically reliant on a set of incorrect assumptions, casting a doubt on the scientific validity of motif-driven discoveries. The implications of these findings are therefore far-reaching across diverse areas of biology.

Coiled-coils refer to a bundle of helices coiled together like strands of a rope. It has been estimated that nearly 3% of protein-encoding regions of genes harbour coiled-coil domains (CCDs). Experimental studies have confirmed that CCDs play a fundamental role in subcellular infrastructure and controlling trafficking of eukaryotic cells. Given the importance of coiled-coils, multiple bioinformatics tools have been developed to facilitate the systematic and high-throughput prediction of CCDs in proteins. In this article, we review and compare 12 sequence-based bioinformatics approaches and tools for coiled-coil prediction. These approaches can be categorized into two classes: coiled-coil detection and coiled-coil oligomeric state prediction. We evaluated and compared these methods in terms of their input/output, algorithm, prediction performance, validation methods and software utility. All the independent testing data sets are available at http://lightning.med.monash.edu/coiledcoil/. In addition, we conducted a case study of nine human polyglutamine (PolyQ) disease-related proteins and predicted CCDs and oligomeric states using various predictors. Prediction results for CCDs were highly variable among different predictors. Only two peptides from two proteins were confirmed to be CCDs by majority voting. Both domains were predicted to form dimeric coiled-coils using oligomeric state prediction. We anticipate that this comprehensive analysis will be an insightful resource for structural biologists with limited prior experience in bioinformatics tools, and for bioinformaticians who are interested in designing novel approaches for coiled-coil and its oligomeric state prediction.

The enabling of scientific experiments that are embarrassingly parallel, long running and data-intensive into a cloud-based execution environment is a desirable, though complex undertaking for many researchers. The management of such virtual environments is cumbersome and not necessarily within the core skill set for scientists and engineers. We present here Chiminey, a software platform that enables researchers to (i) run applications on both traditional high-performance computing and cloud-based computing infrastructures, (ii) handle failure during execution, (iii) curate and visualise execution outputs, (iv) share such data with collaborators or the public, and (v) search for publicly available data.

The crystal structures of unliganded and liganded pMHC molecules provide a structural basis for TCR recognition yet they represent 'snapshots' and offer limited insight into dynamics that may be important for interaction and T cell activation. MHC molecules HLA-B*3501 and HLA-B*3508 both bind a 13 mer viral peptide (LPEP) yet only HLA-B*3508-LPEP induces a CTL response characterised by the dominant TCR clonetype SB27. HLA-B*3508-LPEP forms a tight and long-lived complex with SB27, but the relatively weak interaction between HLA-B*3501-LPEP and SB27 fails to trigger an immune response. HLA-B*3501 and HLA-B*3508 differ by only one amino acid (L/R156) located on α2-helix, but this does not alter the MHC or peptide structure nor does this polymorphic residue interact with the peptide or SB27. In the absence of a structural rationalisation for the differences in TCR engagement we performed a molecular dynamics study of both pMHC complexes and HLA-B*3508-LPEP in complex with SB27. This reveals that the high flexibility of the peptide in HLA-B*3501 compared to HLA-B*3508, which was not apparent in the crystal structure alone, may have an under-appreciated role in SB27 recognition. The TCR pivots atop peptide residues 6-9 and makes transient MHC contacts that extend those observed in the crystal structure. Thus MD offers an insight into 'scanning' mechanism of SB27 that extends the role of the germline encoded CDR2α and CDR2β loops. Our data are consistent with the vast body of experimental observations for the pMHC-LPEP-SB27 interaction and provide additional insights not accessible using crystallography.

Dihydrodipicolinate synthase (DHDPS) is an essential enzyme involved in the lysine biosynthesis pathway. DHDPS from E. coli is a homotetramer consisting of a 'dimer of dimers', with the catalytic residues found at the tight-dimer interface. Crystallographic and biophysical evidence suggest that the dimers associate to stabilise the active site configuration, and mutation of a central dimer-dimer interface residue destabilises the tetramer, thus increasing the flexibility and reducing catalytic efficiency and substrate specificity. This has led to the hypothesis that the tetramer evolved to optimise the dynamics within the tight-dimer. In order to gain insights into DHDPS flexibility and its relationship to quaternary structure and function, we performed comparative Molecular Dynamics simulation studies of native tetrameric and dimeric forms of DHDPS from E. coli and also the native dimeric form from methicillin-resistant Staphylococcus aureus (MRSA). These reveal a striking contrast between the dynamics of tetrameric and dimeric forms. Whereas the E. coli DHDPS tetramer is relatively rigid, both the E. coli and MRSA DHDPS dimers display high flexibility, resulting in monomer reorientation within the dimer and increased flexibility at the tight-dimer interface. The mutant E. coli DHDPS dimer exhibits disorder within its active site with deformation of critical catalytic residues and removal of key hydrogen bonds that render it inactive, whereas the similarly flexible MRSA DHDPS dimer maintains its catalytic geometry and is thus fully functional. Our data support the hypothesis that in both bacterial species optimal activity is achieved by fine tuning protein dynamics in different ways: E. coli DHDPS buttresses together two dimers, whereas MRSA dampens the motion using an extended tight-dimer interface.

MOTIVATION: Identification of functionally equivalent genes in different species is essential to understand the evolution of biological pathways and processes. At the same time, identification of strings of conserved orthologous genes helps identify complex genomic rearrangements across different organisms. Such an insight is particularly useful, for example, in the transfer of experimental results between different experimental systems such as Drosophila and mammals. RESULTS: Here, we describe the Encapsulated Gene-by-gene Matching (EGM) approach, a method that employs a graph matching strategy to identify gene orthologs and conserved gene segments. Given a pair of genomes, EGM constructs a global gene match for all genes taking into account gene context and family information. The Hungarian method for identifying the maximum weight matching in bipartite graphs is employed, where the resulting matching reveals one-to-one correspondences between nodes (genes) in a manner that maximizes the gene similarity and context. CONCLUSION: We tested our approach by performing several comparisons including a detailed Human versus Mouse genome mapping. We find that the algorithm is robust and sensitive in detecting orthologs and conserved gene segments. EGM can sensitively detect rearrangements within large and small chromosomal segments. The EGM tool is fully automated and easy to use compared to other more complex methods that also require extensive manual intervention and input. AVAILABILITY: The EGM software, Supplementary information and other tools are available online from http://vbc.med.monash.edu.au/ approximately kmahmood/EGM.

Determining the X-ray crystallographic structures of proteins using the technique of molecular replacement (MR) can be a time and labor-intensive trial-and-error process, involving evaluating tens to hundreds of possible solutions to this complex 3D jigsaw puzzle. For challenging cases indicators of success often do not appear until the later stages of structure refinement, meaning that weeks or even months could be wasted evaluating MR solutions that resist refinement and do not lead to a final structure. In order to improve the chances of success as well as decrease this timeframe, we have developed a novel grid computing approach that performs many MR calculations in parallel, speeding up the process of structure determination from weeks to hours. This high-throughput approach also allows parameter sweeps to be performed in parallel, improving the chances of MR success.

With new scientific instruments growing exponentially in their capability to generate research data, new infrastructure needs to be developed and deployed to allow researchers to effectively and securely manage their research data from collection to publication. In particular, researchers need to be able to easily acquire data from instruments, store and manage potentially large quantities of data, easily process the data, share research resources and work spaces with colleagues both inside and outside of their institution, search and discover across their accessible collections, and easily publish datasets and related research artefacts. The ARCHER Project has developed production-ready generic e-Research infrastructure including: a Research Repository; Scientific Dataset Managers (both a web and desktop application); Distributed Integrated Multi-Sensor & Instrument Middleware; and a Collaborative Workspace Environment. Institutions can selectively deploy these components to greatly assist their researchers in managing their research data.

In the past decade Grid computing has matured considerably. A number of groups have built, operated, and expanded large testbed and production Grids. These Grids have inevitably been designed to meet the needs of a limited set of initial stakeholders, resulting in varying and sometimes ad-hoc specifications. As the use of e-Science becomes more common, this inconsistency is increasingly problematic for the growing set of applications requiring more resources than a single Grid can offer, as spanning these Grid islands is far from trivial. Thus, Grid interoperability is attracting much interest as researchers try to build bridges between separate Grids. Recently we ran a case study that tested interoperation between several Grids, during which we recorded and classified the issues that arose. In this paper we provide empirical evidence supporting existing interoperability efforts, and identify current and potential barriers to Grid interoperability.

Structural biology research places significant demands upon computing and informatics infrastructure. Protein production, crystallization and X-ray data collection require solutions to data management, annotation, target tracking and remote experiment monitoring. Structure elucidation is computationally demanding and requires user-friendly interfaces to high-performance computing resources. Here we discuss how these challenges are being met at the Protein Crystallography Unit at Monash University. Specifically, we have developed informatics solutions for each stage in the structural biology pipeline, from DNA cloning through to protein structure determination. This infrastructure will be pivotal for accelerating the process of structural discovery and will be of significant interest to other laboratories worldwide.