MOTIVATION: The β-barrel is a ubiquitous fold that is deployed to accomplish a wide variety of biological functions including membrane-embedded pores. Key influences of β-barrel lumen diameter include the number of β-strands (n) and the degree of shear (S), the latter value measuring the extent to which the β-sheet is tilted within the β-barrel. Notably, it has previously been reported that the shear value for small antiparallel β-barrels (n≤24) typically ranges between n and 2n. Conversely, it has been suggested that the β-strands in giant antiparallel β-barrels, such as those formed by pore forming cholesterol-dependent cytolysins (CDC), are parallel relative to the axis of the β-barrel, i.e. S=0. The S=0 arrangement, however, has never been observed in crystal structures of small β-barrels. Therefore, the structural basis for how CDCs form a β-barrel and span a membrane remains to be understood. RESULTS: Through comparison of molecular models with experimental data, we are able to identify how giant CDC β-barrels utilize a 'near parallel' arrangement of β-strands where S=n/2. Furthermore, we show how side-chain packing within the β-barrel lumen is an important limiting factor with respect to the possible shear values for small β-barrels (n≤24 β-strands). In contrast, our models reveal no such limitation restricts the shear value of giant β-barrels (n>24 β-strands). Giant β-barrels can thus access a different architecture compared with smaller β-barrels.

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.

MOTIVATION: The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates. RESULTS: In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship. AVAILABILITY: http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/ CONTACT: [email protected]; [email protected]; james; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.