Integrating shotgun proteomics and mRNA expression data to improve protein identification

Motivation: Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration

Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm

A longstanding question in molecular biology is the extent to which the behavior of macromolecules observed in vitro accurately reflects their behavior in vivo. A number of sophisticated experimental techniques now allow the behavior of individual types of macromolecule to be studied directly in vivo; none, however, allow a wide range of molecule types to be observed simultaneously. In order to tackle this issue we have adopted a computational perspective, and, having selected the model prokaryote Escherichia coli as a test system, have assembled an atomically detailed model of its cytoplasmic environment that includes 50 of the most abundant types of macromolecules at experimentally measured concentrations. Brownian dynamics (BD) simulations of the cytoplasm model have been calibrated to reproduce the translational diffusion coefficients of Green Fluorescent Protein (GFP) observed in vivo, and “snapshots” of the simulation trajectories have been used to compute the cytoplasm's effects on the thermodynamics of protein folding, association and aggregation events. The simulation model successfully describes the relative thermodynamic stabilities of proteins measured in E. coli, and shows that effects additional to the commonly cited “crowding” effect must be included in attempts to understand macromolecular behavior in vivo

Coarsegrained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm

We present a general-purpose model for biomolecular simulations at the molecular level that incorporates stochasticity, spatial dependence, and volume exclusion, using diffusing and reacting particles with physical dimensions. To validate the model, we first established the formal relationship between the microscopic model parameters (timestep, move length, and reaction probabilities) and the macroscopic coefficients for diffusion and reaction rate. We then compared simulation results with Smoluchowski theory for diffusion-limited irreversible reactions and the best available approximation for diffusion-influenced reversible reactions. To simulate the volumetric effects of a crowded intracellular environment, we created a virtual cytoplasm composed of a heterogeneous population of particles diffusing at rates appropriate to their size. The particle-size distribution was estimated from the relative abundance, mass, and stoichiometries of protein complexes using an experimentally derived proteome catalog from Escherichia coli K12. Simulated diffusion constants exhibited anomalous behavior as a function of time and crowding. Although significant, the volumetric impact of crowding on diffusion cannot fully account for retarded protein mobility in vivo, suggesting that other biophysical factors are at play. The simulated effect of crowding on barnase-barstar dimerization, an experimentally characterized example of a bimolecular association reaction, reveals a biphasic time course, indicating that crowding exerts different effects over different timescales. These observations illustrate that quantitative realism in biosimulation will depend to some extent on mesoscale phenomena that are not currently well understood

RasGRPs are targets of the anti-cancer agent ingenol-3-angelate.

Ingenol-3-angelate (I3A) is a non-tumor promoting phorbol ester-like compound identified in the sap of Euphoria peplus. Similar to tumor promoting phorbol esters, I3A is a diacylglycerol (DAG) analogue that binds with high affinity to the C1 domains of PKCs, recruits PKCs to cellular membranes and promotes enzyme activation. Numerous anti-cancer activities have been attributed to I3A and ascribed to I3A's effects on PKCs. We show here that I3A also binds to and activates members of the RasGRP family of Ras activators leading to robust elevation of Ras-GTP and engagement of the Raf-Mek-Erk kinase cascade. In response to I3A, recombinant proteins consisting of GFP fused separately to full-length RasGRP1 and RasGRP3 were rapidly recruited to cell membranes, consistent with direct binding of the compound to RasGRP's C1 domain. In the case of RasGRP3, IA3 treatment led to positive regulatory phosphorylation on T133 and activation of the candidate regulatory kinase PKCδ. I3A treatment of select B non-Hodgkin's lymphoma cell lines resulted in quantitative and qualitative changes in Bcl-2 family member proteins and induction of apoptosis, as previously demonstrated with the DAG analogue bryostatin 1 and its synthetic analogue pico. Our results offer further insights into the anticancer properties of I3A, support the idea that RasGRPs represent potential cancer therapeutic targets along with PKC, and expand the known range of ligands for RasGRP regulation