Rapid and sustained nuclear–cytoplasmic ERK oscillations induced by epidermal growth factor

Although the ERK pathway has a central role in the response of cells to growth factors, its regulatory structure and dynamics are incompletely understood. To investigate ERK activation in real time, we expressed an ERK–GFP fusion protein in human mammary epithelial cells. On EGF stimulation, we observed sustained oscillations of the ERK–GFP fusion protein between the nucleus and cytoplasm with a periodicity of ∼15 min. The oscillations were persistent (>45 cycles), independent of cell cycle phase, and were highly dependent on cell density, essentially disappearing at confluency. Oscillations occurred even at ligand doses that elicited very low levels of ERK phosphorylation, and could be detected biochemically in both transfected and nontransfected cells. Mathematical modeling revealed that negative feedback from phosphorylated ERK to the cascade input was necessary to match the robustness of the oscillation characteristics observed over a broad range of ligand concentrations. Our characterization of single-cell ERK dynamics provides a quantitative foundation for understanding the regulatory structure of this signaling cascade

A domain-based approach to predict protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.</p> <p>Results</p> <p>DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.</p> <p>Conclusion</p> <p>We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.</p

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Comparison of the strengths of the Munich Information Center for Protein Sequences (MIPS) positive (red line with squares) and negative (blue line with circles) protein-protein interactions computed using the InterDom domain-domain interaction scores

<p><b>Copyright information:</b></p><p>Taken from "A domain-based approach to predict protein-protein interactions"</p><p>http://www.biomedcentral.com/1471-2105/8/199</p><p>BMC Bioinformatics 2007;8():199-199.</p><p>Published online 13 Jun 2007</p><p>PMCID:PMC1919395.</p><p></p> The interactions with a score of zero are not reported. The histogram curves were calculated by binning the logarithm of the protein-protein interaction scores that were computed using the maximum-score detection rule. Vertical axis shows the percentage of the PPIs with interaction scores that are within the strength interval of a particular bin. Top: Yeast PPI; Bottom: Human PPI

Comparison of the strengths of the MIPS positive (red line with squares) and negative (blue line with circles) protein-protein interactions computed using the DomainGA optimized domain-domain interaction scores

<p><b>Copyright information:</b></p><p>Taken from "A domain-based approach to predict protein-protein interactions"</p><p>http://www.biomedcentral.com/1471-2105/8/199</p><p>BMC Bioinformatics 2007;8():199-199.</p><p>Published online 13 Jun 2007</p><p>PMCID:PMC1919395.</p><p></p> Vertical axis shows the percentage of the PPIs with interaction scores that were calculated by binning the total protein-protein interaction scores using unit bin sizes. Top: Inclusive set yeast PPI; Bottom: Closed set human PPI

Cell surface receptors for signal transduction and ligand transport: a design principles study.

Receptors constitute the interface of cells to their external environment. These molecules bind specific ligands involved in multiple processes, such as signal transduction and nutrient transport. Although a variety of cell surface receptors undergo endocytosis, the systems-level design principles that govern the evolution of receptor trafficking dynamics are far from fully understood. We have constructed a generalized mathematical model of receptor-ligand binding and internalization to understand how receptor internalization dynamics encodes receptor function and regulation. A given signaling or transport receptor system represents a particular implementation of this module with a specific set of kinetic parameters. Parametric analysis of the response of receptor systems to ligand inputs reveals that receptor systems can be characterized as being: i) avidity-controlled where the response control depends primarily on the extracellular ligand capture efficiency, ii) consumption-controlled where the ability to internalize surface-bound ligand is the primary control parameter, and iii) dual-sensitivity where both the avidity and consumption parameters are important. We show that the transferrin and low-density lipoprotein receptors are avidity-controlled, the vitellogenin receptor is consumption-controlled, and the epidermal growth factor receptor is a dual-sensitivity receptor. Significantly, we show that ligand-induced endocytosis is a mechanism to enhance the accuracy of signaling receptors rather than merely serving to attenuate signaling. Our analysis reveals that the location of a receptor system in the avidity-consumption parameter space can be used to understand both its function and its regulation

Comparison of the mean scores of the parameters that were optimized using the 344 parameter closed set training data with different fitness functions

<p><b>Copyright information:</b></p><p>Taken from "A domain-based approach to predict protein-protein interactions"</p><p>http://www.biomedcentral.com/1471-2105/8/199</p><p>BMC Bioinformatics 2007;8():199-199.</p><p>Published online 13 Jun 2007</p><p>PMCID:PMC1919395.</p><p></p> X-axis: Optimization using both the negative and positive PPIs with the maximum score detection rule (as in Figure 4). Y-axis: Optimization with the minimum parameter magnitude fitness function using only the positive PPI list. The maximum value of the color scale is lowered from 121 to 30 to enhance the contrast between the histogram points