James D. Watson

ProFunc: a server for predicting protein function from 3D structure.

ProFunc () is a web server for predicting the likely function of proteins whose 3D structure is known but whose function is not. Users submit the coordinates of their structure to the server in PDB format. ProFunc makes use of both existing and novel methods to analyse the proteins sequence and structure identifying functional motifs or close relationships to functionally characterized proteins. A summary of the analyses provides an at-a-glance view of what each of the different methods has found. More detailed results are available on separate pages. Often where one method has failed to find anything useful another may be more forthcoming. The server is likely to be of most use in structural genomics where a large proportion of the proteins whose structures are solved are of hypothetical proteins of unknown function. However, it may also find use in a comparative analysis of members of large protein families. It provides a convenient compendium of sequence and structural information that often hold vital functional clues to be followed up experimentally.

Protein function annotation by homology-based inference.

With many genomes now sequenced, computational annotation methods to characterize genes and proteins from their sequence are increasingly important. The BioSapiens Network has developed tools to address all stages of this process, and here we review progress in the automated prediction of protein function based on protein sequence and structure.

The Shwachman-Bodian-Diamond Syndrome Protein Family Is Involved in RNA Metabolism

A combination of structural, biochemical, and genetic studies in model organisms was used to infer a cellular role for the human protein (SBDS) responsible for Shwachman-Bodian-Diamond syndrome. The crystal structure of the SBDS homologue in Archaeoglobus fulgidus, AF0491, revealed a three domain protein. The N-terminal domain, which harbors the majority of disease-linked mutations, has a novel three-dimensional fold. The central domain has the common winged helix-turn-helix motif, and the C-terminal domain shares structural homology with known RNA-binding domains. Proteomic analysis of the SBDS sequence homologue in Saccharomyces cerevisiae, YLR022C, revealed an association with over 20 proteins involved in ribosome biosynthesis. NMR structural genomics revealed another yeast protein, YHR087W, to be a structural homologue of the AF0491 N-terminal domain. Sequence analysis confirmed them as distant sequence homologues, therefore related by divergent evolution. Synthetic genetic array analysis of YHR087W revealed genetic interactions with proteins involved in RNA and rRNA processing including Mdm20/Nat3, Nsr1, and Npl3. Our observations, taken together with previous reports, support the conclusion that SBDS and its homologues play a role in RNA metabolism.

From protein structure to biochemical function

Here we describe various methods currently under development aimed at identifying a protein’s function from its three-dimensional structure. We are combining a number of these methods to create a pipeline of applications, called ProFunc, which will take a given 3D structure, run all the applications on it and compile and summarise the results obtained. The aim is to provide a best guess as to the protein’s function from the evidence provided by the different methods. Here we present three examples, using structures solved by the Midwest Center for Structural Genomics consortium, illustrating the strengths and weaknesses of current approaches.

Bioinformatics training: a review of challenges, actions and support requirements

As bioinformatics becomes increasingly central to research in the molecular life sciences, the need to train non-bioinformaticians to make the most of bioinformatics resources is growing. Here, we review the key challenges and pitfalls to providing effective training for users of bioinformatics services, and discuss successful training strategies shared by a diverse set of bioinformatics trainers. We also identify steps that trainers in bioinformatics could take together to advance the state of the art in current training practices. The ideas presented in this article derive from the first Trainer Networking Session held under the auspices of the EU-funded SLING Integrating Activity, which took place in November 2009.

pLEF, a novel vector for expression of glutathione S-transferase fusion proteins in mammalian cells *

An expression vector, pLEF, has been used to produce the intracellular domain (IC) of the human CD95 (Fas/APO-1) apoptosis receptor as a glutathione S-transferase (GST) fusion protein in murine L929 fibroblasts. GST::CD95IC was affinity-purified in a single step using glutathione-Sepharose. Purification of GST::CD95IC from 32P-labelled L929 cells and cleavage with thrombin revealed that CD95IC was phosphorylated in vivo when produced as a GST fusion protein. Therefore, pLEF may facilitate the mapping of in vivo-modified sites of eukaryotic proteins.

Bioinformatics Training Network (BTN): a community resource for bioinformatics trainers

Funding bodies are increasingly recognizing the need to provide graduates and researchers with access to short intensive courses in a variety of disciplines, in order both to improve the general skills base and to provide solid foundations on which researchers may build their careers. In response to the development of ‘high-throughput biology’, the need for training in the field of bioinformatics, in particular, is seeing a resurgence: it has been defined as a key priority by many Institutions and research programmes and is now an important component of many grant proposals. Nevertheless, when it comes to planning and preparing to meet such training needs, tension arises between the reward structures that predominate in the scientific community which compel individuals to publish or perish, and the time that must be devoted to the design, delivery and maintenance of high-quality training materials. Conversely, there is much relevant teaching material and training expertise available worldwide that, were it properly organized, could be exploited by anyone who needs to provide training or needs to set up a new course. To do this, however, the materials would have to be centralized in a database and clearly tagged in relation to target audiences, learning objectives, etc. Ideally, they would also be peer reviewed, and easily and efficiently accessible for downloading. Here, we present the Bioinformatics Training Network (BTN), a new enterprise that has been initiated to address these needs and review it, respectively, to similar initiatives and collections.

Solution Structure of the Inner DysF Domain of Myoferlin and Implications for Limb Girdle Muscular Dystrophy Type 2B

Mutations in the protein dysferlin, a member of the ferlin family, lead to limb girdle muscular dystrophy type 2B and Myoshi myopathy. The ferlins are large proteins characterised by multiple C2 domains and a single C-terminal membrane-spanning helix. However, there is sequence conservation in some of the ferlin family in regions outside the C2 domains. In one annotation of the domain structure of these proteins, an unusual internal duplication event has been noted where a putative domain is inserted in between the N- and C-terminal parts of a homologous domain. This domain is known as the DysF domain. Here, we present the solution structure of the inner DysF domain of the dysferlin paralogue myoferlin, which has a unique fold held together by stacking of arginine and tryptophans, mutations that lead to clinical disease in dysferlin.

Target selection and determination of function in structural genomics.

The first crucial step in any structural genomics project is the selection and prioritization of target proteins for structure determination. There may be a number of selection criteria to be satisfied, including that the proteins have novel folds, that they be representatives of large families for which no structure is known, and so on. The better the selection at this stage, the greater is the value of the structures obtained at the end of the experimental process. This value can be further enhanced once the protein structures have been solved if the functions of the given proteins can also be determined. Here we describe the methods used at either end of the experimental process: firstly, sensitive sequence comparison techniques for selecting a high‐quality list of target proteins, and secondly the various computational methods that can be applied to the eventual 3D structures to determine the most likely biochemical function of the proteins in question. IUBMB Life, 55: 249‐255, 2003

Antigen-induced interferon-γ and interleukin-2 responses of cattle inoculated with Mycobacterium bovis

Bovine purified protein derivative (PPD)-induced interferon-gamma (IFN-gamma) and interleukin-2 (IL-2) mRNA expression was measured in peripheral blood lymphocyte cultures of cattle inoculated with Mycobacterium bovis and compared to cytokine protein levels as measured by IFN-gamma enzyme-linked immunosorbent assay and IL-2 bioassay. For individual animals, positive correlations were observed between mRNA and protein levels of bovine PPD-induced IFN-gamma and IL-2, although the correlations were stronger for IFN-gamma. Expression of these two cytokines also correlated with responses from a comparative intradermal test and a M. bovis antibody test. At 7 and 20 weeks after inoculation, bovine PPD-induced IFN-gamma and IL-2 mRNA expression was detected in all animals with tuberculous lesions and in a proportion of the M. bovis-inoculated animals with no lesions. Correlation of antigen-induced IFN-gamma and IL-2 with other immune parameters suggests that these two cytokines play an important role in the immune response to bovine tuberculosis.