Thylakoid membrane architecture

Confocal scanning laser microscopic observations were made on live chloroplasts in intact cells and on mechanically isolated, intact chloroplasts. Chlorophyll fluorescence was imaged to observe thylakoid membrane architecture. C-3 plant species studied included Spinacia oleracea L., Spathiphyllum sp. Schott, cv. 'Mauna Loa', and Pisum sativum L. C-4 plants were also investigated: Saccharum officinarum L., Sorghum bicolor L. Moench, Zea mays L. and Panicum miliaceum L. Some Spinacia chloroplasts were treated with 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) to enhance or sodium dithionite (SD) to reduce the photosystem II fluorescence signal. Confocal microscopy images of C-3 chloroplasts differed from electron microscopy pictures because they showed discrete spots of bright fluorescence with black regions between them. There was no evidence of fluorescence from stroma thylakoids. The thylakoid membrane system at times appeared to be string-like, with brightly fluorescing grana lined up like beads. C-4 bundle sheath chloroplasts were imaged from three different types of C-4 plants. Saccharum and Sorghum bundle sheath chloroplasts showed homogeneous fluorescence and were much dimmer than mesophyll chloroplasts. Zea had rudimentary grana, and dim, homogeneous intergrana fluorescence was visualised. Panicum contained thylakoids similar in appearance and string-like arrangement to mesophyll chloroplasts. Isolated Pisum chloroplasts, treated with a drop of 5 mM MgCl2 showed a thylakoid membrane system which appeared to be unravelling. Spongy mesophyll chloroplasts of Spinacia treated with 5 mM sodium dithionite showed a granal thylakoid system with distinct regions of no fluorescence. A time-series experiment provided evidence of dynamic membrane rearrangements over a period of half an hour.

MHCWeb: Converting a WWW database into a knowledge-based collaborative environment

The World Wide Web (WWW) is useful for distributing scientific data. Most existing web data resources organize their information either in structured flat files or relational databases with basic retrieval capabilities. For databases with one or a few simple relations, these approaches are successful, but they can be cumbersome when there is a data model involving multiple relations between complex data. We believe that knowledge-based resources offer a solution in these cases. Knowledge bases have explicit declarations of the concepts in the domain, along with the relations between them. They are usually organized hierarchically, and provide a global data model with a controlled vocabulary, We have created the OWEB architecture for building online scientific data resources using knowledge bases. OWEB provides a shell for structuring data, providing secure and shared access, and creating computational modules for processing and displaying data. In this paper, we describe the translation of the online immunological database MHCPEP into an OWEB system called MHCWeb. This effort involved building a conceptual model for the data, creating a controlled terminology for the legal values for different types of data, and then translating the original data into the new structure. The 0 WEB environment allows for flexible access to the data by both users and computer programs.

A computational analysis of substrate binding strength by phosphorylase kinase and protein kinase A

Protein kinases exhibit various degrees of substrate specificity. The large number of different protein kinases in the eukaryotic proteomes makes it impractical to determine the specificity of each enzyme experimentally. To test if it were possible to discriminate potential substrates from non-substrates by simple computational techniques, we analysed the binding enthalpies of modelled enzyme-substrate complexes and attempted to correlate it with experimental enzyme kinetics measurements. The crystal structures of phosphorylase kinase and cAMP-dependent protein kinase were used to generate models of the enzyme with a series of known peptide substrates and non-substrates, and the approximate enthalpy of binding assessed following energy minimization. We show that the computed enthalpies do not correlate closely with kinetic measurements, but the method can distinguish good substrates from weak substrates and non-substrates. Copyright (C) 2002 John Wiley Sons, Ltd.

Computational immunology: The coming of age

The explosive growth in biotechnology combined with major advancesin information technology has the potential to radically transformimmunology in the postgenomics era. Not only do we now have readyaccess to vast quantities of existing data, but new data with relevanceto immunology are being accumulated at an exponential rate. Resourcesfor computational immunology include biological databases and methodsfor data extraction, comparison, analysis and interpretation. Publiclyaccessible biological databases of relevance to immunologists numberin the hundreds and are growing daily. The ability to efficientlyextract and analyse information from these databases is vital forefficient immunology research. Most importantly, a new generationof computational immunology tools enables modelling of peptide transportby the transporter associated with antigen processing (TAP), modellingof antibody binding sites, identification of allergenic motifs andmodelling of T-cell receptor serial triggering.

Computational tools for the study of allergens

Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

PETAL LOSS, a trihelix transcription factor gene, regulates perianth architecture in the Arabidopsis flower

Perianth development is specifically disrupted in mutants of the PETAL LOSS (PTL) gene, particularly petal initiation and orientation. We have cloned PTL and show that it encodes a plant-specific trihelix transcription factor, one of a family previously known only as regulators of light-controlled genes. PTL transcripts were detected in the early-developing flower, in four zones between the initiating sepals and in their developing margins. Strong misexpression of PTL in a range of tissues universally results in inhibition of growth, indicating that its normal role is to suppress growth between initiating sepals, ensuring that they remain separate. Consistent with this, sepals are sometimes fused in ptl single mutants, but much more frequently in double mutants with either of the organ boundary genes cup-shaped cotyledon1 or 2. Expression of PTL within the newly arising sepals is apparently prevented by the PINOID auxin-response gene. Surprisingly, PTL expression could not be detected in petals during the early stages of their development, so petal defects associated with PTL loss of function may be indirect, perhaps involving disruption to signalling processes caused by overgrowth in the region. PTL-driven reporter gene expression was also detected at later stages in the margins of expanding sepals, petals and stamens, and in the leaf margins; thus, PTL may redundantly dampen lateral outgrowth of these organs, helping define their final shape.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.