SecG function and phospholipid metabolism in Escherichia coli

SecG is an auxiliary protein in the Sec-dependent protein export pathway of Escherichia coli. Although the precise function of SecG is unknown, it stimulates translocation activity and has been postulated to enhance the membrane insertion-deinsertion cycle of SecA. Deletion of secG was initially reported to result in a severe export defect and cold sensitivity. Later results demonstrated that both of these phenotypes were strain dependent, and it was proposed that an additional mutation was required for manifestation of the cold-sensitive phenotype. The results presented here demonstrate that the cold-sensitive secG deletion strain also contains a mutation in glpR that causes constitutive expression of the glp regulon. Introduction of both the glpR mutation and the secG deletion into a wild-type strain background produced a cold-sensitive phenotype, confirming the hypothesis that a second mutation (glpR) contributes to the cold-sensitive phenotype of secG deletion strains. It was speculated that the glpR mutation causes an intracellular depletion of glycerol-3-phosphate due to constitutive synthesis of GlpD and subsequent channeling of glycerol-3-phosphate into metabolic pathways. In support of this hypothesis, it was demonstrated that addition of glycerol-3-phosphate to the growth medium ameliorated the cold sensitivity, as did introduction of a glpD mutation. This depletion of glycerol-3-phosphate is predicted to limit phospholipid biosynthesis, causing an imbalance in the levels of membrane phospholipids. It is hypothesized that this state of phospholipid imbalance imparts a dependence on SecG for proper function or stabilization of the translocation apparatus.     

Integrative bioinformatics for functional genome annotation: trawling for G protein-coupled receptors

G protein-coupled receptors (GPCR) are amongst the best studied and most functionally diverse types of cell-surface protein. The importance of GPCRs as mediates or cell function and organismal developmental underlies their involvement in key physiological roles and their prominence as targets for pharmacological therapeutics. In this review, we highlight the requirement for integrated protocols which underline the different perspectives offered by different sequence analysis methods. BLAST and FastA offer broad brush strokes. Motif-based search methods add the fine detail. Structural modelling offers another perspective which allows us to elucidate the physicochemical properties that underlie ligand binding. Together, these different views provide a more informative and a more detailed picture of GPCR structure and function. Many GPCRs remain orphan receptors with no identified ligand, yet as computer-driven functional genomics starts to elaborate their functions, a new understanding of their roles in cell and developmental biology will follow.     

PRINTS and its automatic supplement,prePRINTS

The PRINTS database houses a collection of protein fingerprints. These may be used to assign uncharacterised sequences to known families and hence to infer tentative functions. The September 2002 release (version 36.0) includes 1800 fingerprints, encoding approximately 11 000 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. In addition to its continued steady growth, we report here the development of an automatic supplement, prePRINTS, designed to increase the coverage of the resource and reduce some of the manual burdens inherent in its maintenance. The databases are accessible for interrogation and searching at http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/.     

BPROMPT: A consensus server for membrane protein prediction

Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Bayesian Belief Network to combine the results of other prediction methods, providing a more accurate consensus prediction. Topology predictions with accuracies of 70% for prokaryotes and 53% for eukaryotes were achieved. BPROMPT can be accessed at http://www.jenner.ac.uk/BPROMPT.     

Bacterial bioinformatics: pathogenesis and the genome

As the number of completed microbial genome sequences continues to grow, there is a pressing need for the exploitation of this wealth of data through a synergistic interaction between the well-established science of bacteriology and the emergent discipline of bioinformatics. Antibiotic resistance and pathogenicity in virulent bacteria has become an increasing problem, with even the strongest drugs useless against some species, such as multi-drug resistant Enterococcus faecium and Mycobacterium tuberculosis. The global spread of Human Immunodeficiency Virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS) has contributed to the re-emergence of tuberculosis and the threat from new and emergent diseases. To address these problems, bacterial pathogenicity requires redefinition as Koch's postulates become obsolete. This review discusses how the use of bacterial genomic information, and the in silico tools available at present, may aid in determining the definition of a current pathogen. The combination of both fields should provide a rapid and efficient way of assisting in the future development of antimicrobial therapies.     

MHCPred: bringing a quantitative dimension to the online prediction of MHC binding

The accurate prediction of T cell epitopes is one of the key aspirations of immunoinformatics. We have developed a partial least squares-based, robust multivariate statistical method for the quantitative prediction of peptide binding to major histocompatibility complexes (MHCs), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available as a World Wide Web server.     

Increased apoptosis in U937 cells over-expressing lipocortin 1 (annexin I)

The potential involvement of endogenous lipocortin 1 in the process of cellular apoptosis, particularly in cells of the myelo-monocytic lineage, has been investigated. U937 cells were transfected either with an antisense or a sense DNA for lipocortin 1 and the stable clones 36.4AS clone (20-40% lower lipocortin 1 levels) and 15S (30% higher lipocortin 1 levels) were obtained. Cell apoptosis was induced by incubation with tumor necrosis factor-alpha: optimal responses were observed within a 24 h incubation period at a 5 ng/ml concentration. Apoptosis was assessed both morphologically, by annexin V binding and cell cycle analysis with propidium iodide. Whilst no consistent difference was seen between wild type cells and clone 36.4AS, a higher incidence of apoptosis (ranging from +30% to + 60%) was observed in the 15S clone. Release of arachidonic acid from loaded cells was promoted by 24 h incubation with the cytokine, and a higher degree of release was measured in the 15S clone. These data indicate that endogenous intracellular lipocortin 1 is involved in the promotion of apoptosis in cells of the myelo-monocytic derivation.     

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.     

VaxiJen: a server for prediction of protective antigens,tumour antigens and subunit vaccines

Background  

Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties.