MOTIVATION: Intrinsically disordered regions are key for the function of numerous proteins. Due to the difficulties in experimental disorder characterization, many computational predictors have been developed with various disorder flavors. Their performance is generally measured on small sets mainly from experimentally solved structures, e.g. Protein Data Bank (PDB) chains. MobiDB has only recently started to collect disorder annotations from multiple experimental structures. RESULTS: MobiDB annotates disorder for UniProt sequences, allowing us to conduct the first large-scale assessment of fast disorder predictors on 25 833 different sequences with X-ray crystallographic structures. In addition to a comprehensive ranking of predictors, this analysis produced the following interesting observations. (i) The predictors cluster according to their disorder definition, with a consensus giving more confidence. (ii) Previous assessments appear over-reliant on data annotated at the PDB chain level and performance is lower on entire UniProt sequences. (iii) Long disordered regions are harder to predict. (iv) Depending on the structural and functional types of the proteins, differences in prediction performance of up to 10% are observed. AVAILABILITY: The datasets are available from Web site at URL: http://mobidb.bio.unipd.it/lsd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal. RESULTS: We have applied a new optimization framework for the Taverna scientific workflow management system (http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction. CONCLUSION: Using the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework.

Scientific workflows have emerged as an important tool for combining computational power with data analysis for all scientific domains in e-science. They help scientists to design and execute complex in silico experiments. However, with increasing complexity it becomes more and more infeasible to optimize scientific workflows by trial and error. To address this issue, this paper describes the design of a new optimization phase integrated in the established scientific workflow life cycle. We have also developed a flexible optimization application programming interface (API) and have integrated it into a scientific workflow management system. A sample plugin for parameter optimization based on genetic algorithms illustrates, how the API enables rapid implementation of concrete workflow optimization methods. Finally, a use case taken from the area of structural bioinformatics validates how the optimization approach facilitates setup, execution and monitoring of workflow parameter optimization in high performance computing e-science environments.

As scientific workflows are becoming more complex and apply compute-intensive methods to increasingly large data volumes, access to HPC resources is becoming mandatory. We describe the development of a novel plug in for the Tavern a workflow system, which provides transparent and secure access to HPC/grid resources via the UNICORE grid middleware, while maintaining the ease of use that has been the main reason for the success of scientific workflow systems. A use case from the bioinformatics domain demonstrates the potential of the UNICORE plug in for Tavern a by creating a scientific workflow that executes the central parts in parallel on a cluster resource.

MOTIVATION: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information. RESULTS: We propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20,000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs. AVAILABILITY: DHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb