The C2 Domain of PKCα Is a Ca2+-dependent PtdIns(4,5)P2 Sensing Domain: A New Insight into an Old Pathway

Departamento de Bioquímica y Biología Molecular (A), Facultad de Veterinaria, Universidad de Murcia Apartado de Correos 4021, E-30100-Murcia, Spain.
Journal of Molecular Biology (Impact Factor: 4.33). 11/2006; 362(5):901-14. DOI: 10.1016/j.jmb.2006.07.093
Source: PubMed


The C2 domain is a targeting domain that responds to intracellular Ca2+ signals in classical protein kinases (PKCs) and mediates the translocation of its host protein to membranes. Recent studies have revealed a new motif in the C2 domain, named the lysine-rich cluster, that interacts with acidic phospholipids. The purpose of this work was to characterize the molecular mechanism by which PtdIns(4,5)P2 specifically interacts with this motif. Using a combination of isothermal titration calorimetry, fluorescence resonance energy transfer and time-lapse confocal microscopy, we show here that Ca2+ specifically binds to the Ca2+ -binding region, facilitating PtdIns(4,5)P2 access to the lysine-rich cluster. The magnitude of PtdIns(4,5)P2 binding is greater than in the case of other polyphosphate phosphatidylinositols. Very importantly, the residues involved in PtdIns(4,5)P2 binding are essential for the plasma membrane localization of PKCalpha when RBL-2H3 cells are stimulated through their IgE receptors. Additionally, CFP-PH and CFP-C1 domains were used as bioprobes to demonstrate the co-existence of PtdIns(4,5)P2 and diacylglycerol in the plasma membrane, and it was shown that although a fraction of PtdIns(4,5)P2 is hydrolyzed to generate diacylglycerol and IP3, an important amount still remains in the membrane where it is available to activate PKCalpha. These findings entail revision of the currently accepted model of PKCalpha recruitment to the membrane and its activation.

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    • "In the presence of DOG a very reduced K0.5 value of 0.11 µM was observed for PIP2, although Vmax increased very slightly as a result of the addition of DOG, confirming that in the presence of PIP2 diacylglycerol is playing a relatively secondary role in the activation of PKCα. Low KD values have been reported for the binding of PIP2 to the isolated C2 domain of PKCα [4], [54] with about 1.9 µM for POPC-POPS-PIP2 vesicles, a value which is compatible with our observations for K0.5. "
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    ABSTRACT: The C2 domain of PKCα possesses two different binding sites, one for Ca(2+) and phosphatidylserine and a second one that binds PIP2 with very high affinity. The enzymatic activity of PKCα was studied by activating it with large unilamellar lipid vesicles, varying the concentration of Ca(2+) and the contents of dioleylglycerol (DOG), phosphatidylinositol 4,5-bisphosphate (PIP2) and phosphadidylserine (POPS) in these model membranes. The results showed that PIP2 increased the Vmax of PKCα and, when the PIP2 concentration was 5 mol% of the total lipid in the membrane, the addition of 2 mol% of DOG did not increase the activity. In addition PIP2 decreases K0.5 of Ca(2+) more than 3-fold, that of DOG almost 5-fold and that of POPS by a half. The K0.5 values of PIP2 amounted to only 0.11 µM in the presence of DOG and 0.39 in its absence, which is within the expected physiological range for the inner monolayer of a mammalian plasma membrane. As a consequence, PKCα may be expected to operate near its maximum capacity even in the absence of a cell signal producing diacylglycerol. Nevertheless, we have shown that the presence of DOG may also help, since the K0.5 for PIP2 notably decreases in its presence. Taken together, these results underline the great importance of PIP2 in the activation of PKCα and demonstrate that in its presence, the most important cell signal for triggering the activity of this enzyme is the increase in the concentration of cytoplasmic Ca(2+).
    PLoS ONE 07/2013; 8(7):e69041. DOI:10.1371/journal.pone.0069041 · 3.23 Impact Factor
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    • "An increasing number of ubiquitous and structurally distinct domains have been found to display lipid binding properties, collectively referred to as membrane-targeting domains (MTDs). MTDs have been identified in the following families: C1 (Cho, 2001; Sanchez-Bautista et al., 2006; Yang and Kazanietz, 2003), C2 (Cho, 2001; Nalefski and Falke, 1996; Rizo and Sudhof, 1998), PH (Ferguson et al., 2000; Lemmon and Ferguson, 2000), FYVE (Fab1/YOTB/Vac1/EEA1) (Stenmark et al., 2002), PX (phox) (Xu et al., 2001), ENTH (Epsin N-terminal homology)(Camilli et al., 2002), and recently PDZ domains (Chen et al., 2012). Despite their highly similar intra-family folds, not all domains in these families possess membrane-targeting properties. "
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    ABSTRACT: Peripheral membrane-targeting domain (MTD) families, such as C1-, C2- and PH domains, play a key role in signal transduction and membrane trafficking by dynamically translocating their parent proteins to specific plasma membranes when changes in lipid composition occur. It is, however, difficult to determine the subset of domains within families displaying this property, as sequence motifs signifying the membrane binding properties are not well defined. For this reason, procedures based on sequence similarity alone are often insufficient in computational identification of MTDs within families (yielding less than 65% accuracy even with a sequence identity of 70%). We present a machine learning protocol for determining membrane-targeting properties achieving 85-90% accuracy in separating binding and non-binding domains within families. Our model is based on features from both sequence and structure, thereby incorporation statistics obtained from the entire domain family and domain-specific physical quantities such as surface electrostatics. In addition, by using the enriched rules in alternating decision tree classifiers, we are able to determine the meaning of the assigned function labels in terms of biological mechanisms. Conclusions: The high accuracy of the learned models and good agreement between the rules discovered using the ADtree classifier and mechanisms reported in the literature reflect the value of machine learning protocols in both prediction and biological knowledge discovery. Our protocol can thus potentially be used as a general function annotation and knowledge mining tool for other protein domains.
    Bioinformatics 09/2012; 28(18):i431-i437. DOI:10.1093/bioinformatics/bts409 · 4.98 Impact Factor
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    • "The binding characteristics and specificities of C2 domains vary widely. Some have also been reported to bind selectively to phosphoinositides (Sánchez-Bautista et al., 2006), through a basic site that is adjacent to the region that binds Ca 2+ and phosphatidylserine (see below). "
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    ABSTRACT: Regulated relocalization of signaling and trafficking proteins is crucial for the control of many cellular processes and is driven by a series of domains that respond to alterations at membrane surfaces. The first examples of these domains--conditional peripheral membrane proteins--included C1, C2, PH, PX, and FYVE domains, which specifically recognize single tightly regulated membrane components such as diacylglycerol or phosphoinositides. The structural basis for this recognition is now well understood. Efforts to identify additional domains with similar functions that bind other targets (or participate in unexplained cellular processes) have not yielded many more examples of specific phospholipid-binding domains. Instead, most of the recently discovered conditional peripheral membrane proteins bind multiple targets (each with limited specificity), relying on coincidence detection and/or recognizing broader physical properties of the membrane such as charge or curvature. This broader range of recognition modes presents significant methodological challenges for a full structural understanding.
    Structure 12/2011; 20(1):15-27. DOI:10.1016/j.str.2011.11.012 · 5.62 Impact Factor
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