Tim J. Stevens

To address data management and data exchange problems in the nuclear magnetic resonance (NMR) community, the Collaborative Computing Project for the NMR community (CCPN) created a "Data Model" that describes all the different types of information needed in an NMR structural study, from molecular structure and NMR parameters to coordinates. This paper describes the development of a set of software applications that use the Data Model and its associated libraries, thus validating the approach. These applications are freely available and provide a pipeline for high-throughput analysis of NMR data. Three programs work directly with the Data Model: CcpNmr Analysis, an entirely new analysis and interactive display program, the CcpNmr FormatConverter, which allows transfer of data from programs commonly used in NMR to and from the Data Model, and the CLOUDS software for automated structure calculation and assignment (Carnegie Mellon University), which was rewritten to interact directly with the Data Model. The ARIA 2.0 software for structure calculation (Institut Pasteur) and the QUEEN program for validation of restraints (University of Nijmegen) were extended to provide conversion of their data to the Data Model. During these developments the Data Model has been thoroughly tested and used, demonstrating that applications can successfully exchange data via the Data Model. The software architecture developed by CCPN is now ready for new developments, such as integration with additional software applications and extensions of the Data Model into other areas of research.

The various membranes of eukaryotic cells differ in composition, but it is at present unclear if this results in differences in physical properties. The sequences of transmembrane domains (TMDs) of integral membrane proteins should reflect the physical properties of the bilayers in which they reside. We used large datasets from both fungi and vertebrates to perform a comprehensive comparison of the TMDs of proteins from different organelles. We find that TMDs are not generic but have organelle-specific properties with a dichotomy in TMD length between the early and late parts of the secretory pathway. In addition, TMDs from post-ER organelles show striking asymmetries in amino acid compositions across the bilayer that is linked to residue size and varies between organelles. The pervasive presence of organelle-specific features among the TMDs of a particular organelle has implications for TMD prediction, regulation of protein activity by location, and sorting of proteins and lipids in the secretory pathway.

In a recent bioinformatic analysis, we predicted the presence of multiple families of cell surface glycosylphosphatidylinositol (GPI)-anchored proteins (GAPs) in Arabidopsis (G.H.H. Borner, D.J. Sherrier, T.J. Stevens, I.T. Arkin, P. Dupree [2002] Plant Physiol 129: 486-499). A number of publications have since demonstrated the importance of predicted GAPs in diverse physiological processes including root development, cell wall integrity, and adhesion. However, direct experimental evidence for their GPI anchoring is mostly lacking. Here, we present the first, to our knowledge, large-scale proteomic identification of plant GAPs. Triton X-114 phase partitioning and sensitivity to phosphatidylinositol-specific phospholipase C were used to prepare GAP-rich fractions from Arabidopsis callus cells. Two-dimensional fluorescence difference gel electrophoresis and one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis demonstrated the existence of a large number of phospholipase C-sensitive Arabidopsis proteins. Using liquid chromatography-tandem mass spectrometry, 30 GAPs were identified, including six beta-1,3 glucanases, five phytocyanins, four fasciclin-like arabinogalactan proteins, four receptor-like proteins, two Hedgehog-interacting-like proteins, two putative glycerophosphodiesterases, a lipid transfer-like protein, a COBRA-like protein, SKU5, and SKS1. These results validate our previous bioinformatic analysis of the Arabidopsis protein database. Using the confirmed GAPs from the proteomic analysis to train the search algorithm, as well as improved genomic annotation, an updated in silico screen yielded 64 new candidates, raising the total to 248 predicted GAPs in Arabidopsis.