The ability to harness heterogeneous, dynamically available "Grid" resources is attractive to typically resource-starved computational scientists and engineers, as in principle it can increase, by significant factors, the number of cycles that can be delivered to applications. However, new adaptive application structures and dynamic runtime system mechanisms are required if we are to operate effectively in Grid environments. In order to explore some of these issues in a practical setting, we are developing an experimental framework, called Cactus, that incorporates both adaptive application structures for dealing with changing resource characteristics and adaptive resource selection mechanisms that allow applications to change their resource allocations (e.g., via migration) when performance falls outside specified limits. We describe here the adaptive resource selection mechanisms and describe how they are used to achieve automatic application migration to "better" resources following performance degradation. Our results provide insights into the architectural structures required to support adaptive resource selection. In addition, we suggest that this "Cactus Worm" is an interesting challenge problem for Grid computing.

Despite advances in methods and instrumentation for analysis of phosphopeptides using mass spectrometry, it is still difficult to quantify the extent of phosphorylation of a substrate due to physiochemical differences between unphosphorylated and phosphorylated peptides. Here we report experiments to investigate those differences using MALDI-TOF mass spectrometry for a set of synthetic peptides by creating calibration curves of known input ratios of peptides/phosphopeptides and analyzing their resulting signal intensity ratios. These calibration curves reveal subtleties in sequence-dependent differences for relative desorption/ionization efficiencies that cannot be seen from single-point calibrations. We found that the behaviors were reproducible with a variability of 5-10% for observed phosphopeptide signal. Although these data allow us to begin addressing the issues related to modeling these properties and predicting relative signal strengths for other peptide sequences, it is clear this behavior is highly complex and needs to be further explored.

A significant consequence of protein phosphorylation is to alter protein-protein interactions, leading to dynamic regulation of the components of protein complexes that direct many core biological processes. Recent proteomic studies have populated databases with extensive compilations of cellular phosphoproteins and phosphorylation sites and a similarly deep coverage of the subunit compositions and interactions in multiprotein complexes. However, considerably less data are available on the dynamics of phosphorylation, composition of multiprotein complexes or that define their interdependence. We describe a method to identify candidate phosphoprotein complexes by combining phosphoprotein affinity chromatography, separation by size, denaturing gel electrophoresis, protein identification by tandem mass spectrometry, and informatics analysis. Toward developing phosphoproteome profiling, we have isolated native phosphoproteins using a phosphoprotein affinity matrix, Pro-Q Diamond resin (Molecular Probes-Invitrogen). This resin quantitatively retains phosphoproteins and associated proteins from cell extracts. Pro-Q Diamond purification of a yeast whole cell extract followed by 1-D PAGE separation, proteolysis and ESI LC-MS/MS, a method we term PA-GeLC-MS/MS, yielded 108 proteins, a majority of which were known phosphoproteins. To identify proteins that were purified as parts of phosphoprotein complexes, the Pro-Q eluate was separated into two fractions by size, <100 kDa and >100 kDa, before analysis by PAGE and ESI LC-MS/MS and the component proteins queried against databases to identify protein-protein interactions. The <100 kDa fraction was enriched in phosphoproteins indicating the presence of monomeric phosphoproteins. The >100 kDa fraction contained 171 proteins of 20-80 kDa, nearly all of which participate in known protein-protein interactions. Of these 171, few are known phosphoproteins, consistent with their purification by participation in protein complexes. By comparing the results of our phosphoprotein profiling with the informational databases on phosphoproteomics, protein-protein interactions and protein complexes, we have developed an approach to examining the correlation between protein interactions and protein phosphorylation.

α-Synuclein (α-syn) is an intrinsically unstructured 140-residue neuronal protein of uncertain function that is implicated in the etiology of Parkinson’s disease. Tertiary contact formation rate constants in α-syn, determined from diffusion-limited electron-transfer kinetics measurements, are poorly approximated by simple random polymer theory. One source of the discrepancy between theory and experiment may be that interior-loop formation rates are not well approximated by end-to-end contact dynamics models. We have addressed this issue with Monte Carlo simulations to model asynchronous and synchronous motion of contacting sites in a random polymer. These simulations suggest that a dynamical drag effect may slow interior loop formations rates by about a factor of two in comparison to end-to-end loops of comparable size. The additional deviations from random coil behavior in α-syn likely arise from clustering of hydrophobic residues in the disordered polypeptide.