Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Protein Kinase C, Models of

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_193-1


Protein kinase C is a class of protein kinases with 11 isozymes grouped into three subclasses (Newton 2001). The protein kinase C isozymes in the typical group are activated by calcium, diacylglycerol (DAG), and arachidonic acid (AA). The isozymes in the novel group are activated by DAG and AA, but not calcium. The atypical group of isozymes are insensitive to both DAG and calcium. Most models of PKC activity describe binding of calcium and one or two lipids to the PKC holoenzyme.

Detailed Description

All of the protein kinase C isozymes have a similar catalytic domain, and most of the isozymes have two additional domains important for PKC activation: the C2 domain that binds two to three calcium ions and the C1 domain that binds lipids. One additional PKC isozyme is called PKMζ. This is the constitutively active, catalytic domain of typical PKC. This molecule is normally at low levels, and it is “activated” either by cleavage of typical PKC forms or by synthesis.



AMPA Receptor Climbing Fiber Cerebellar Purkinje Cell mGlu5 Receptor Calcium Oscillation 
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  1. Antunes G, De Schutter E (2012) A stochastic signaling network mediates the probabilistic induction of cerebellar long-term depression. J Neurosci 32(27):9288–300Google Scholar
  2. Ajay SM, Bhalla US (2007) A propagating ERKII switch forms zones of elevated dendritic activation correlated with plasticity. HFSP J 1:49–66PubMedCentralPubMedCrossRefGoogle Scholar
  3. Bhalla US, Iyengar R (1999) Emergent properties of networks of biological signaling pathways. Science 283:381–387PubMedCrossRefGoogle Scholar
  4. Chang CW, Poteet E, Schetz JA, Gümüs ZH, Weinstein H (2009) Towards a quantitative representation of the cell signaling mechanisms of hallucinogens: measurement and mathematical modeling of 5-HT1A and 5-HT2A receptor-mediated ERK1/2 activation. Neuropharmacology 56(Suppl 1):213–225PubMedCentralPubMedCrossRefGoogle Scholar
  5. Dupont G, Lokenye EF, Challiss RA (2011) A model for Ca2+ oscillations stimulated by the type 5 metabotropic glutamate receptor: an unusual mechanism based on repetitive, reversible phosphorylation of the receptor. Biochimie 93:2132–2138PubMedCrossRefGoogle Scholar
  6. Kikuchi S, Fujimoto K, Kitagawa N, Fuchikawa T, Abe M, Oka K, Takei K, Tomita M (2003) Kinetic simulation of signal transduction system in hippocampal long-term potentiation with dynamic modeling of protein phosphatase 2A. Neural Netw 16:1389–1398PubMedCrossRefGoogle Scholar
  7. Kim B, Hawes SL, Gillani F, Wallace LJ, Blackwell KT (2013) Signaling pathways involved in striatal synaptic plasticity are sensitive to temporal pattern and exhibit spatial specificity. PLoS Comput Biol 9:e1002953PubMedCentralPubMedCrossRefGoogle Scholar
  8. Kotaleski JH, Lester D, Blackwell KT (2002) Subcellular interactions between parallel fibre and climbing fibre signals in Purkinje cells predict sensitivity of classical conditioning to interstimulus interval. Integr Physiol Behav Sci 37:265–292PubMedCrossRefGoogle Scholar
  9. Kuroda S, Schweighofer N, Kawato M (2001) Exploration of signal transduction pathways in cerebellar long-term depression by kinetic simulation. J Neurosci 21:5693–5702PubMedGoogle Scholar
  10. Newton AC (2001) Protein kinase C: structural and spatial regulation by phosphorylation, cofactors, and macromolecular interactions. Chem Rev 101:2353–2364PubMedCrossRefGoogle Scholar
  11. Oancea E, Meyer T (1998) Protein kinase C as a molecular machine for decoding calcium and diacylglycerol signals. Cell 95:307–318PubMedCrossRefGoogle Scholar
  12. Ogasawara H, Kawato M (2009) Computational models of cerebellar long-term memory. In: Nakanishi S et al (eds) Systems biology. Springer, Tokyo/New York, pp 169–182CrossRefGoogle Scholar
  13. Sajikumar S, Navakkode S, Sacktor TC, Frey JU (2005) Synaptic tagging and cross-tagging: the role of protein kinase Mzeta in maintaining long-term potentiation but not long-term depression. J Neurosci 25:5750–5756PubMedCrossRefGoogle Scholar
  14. Smolen P, Baxter DA, Byrne JH (2012) Molecular constraints on synaptic tagging and maintenance of long-term potentiation: a predictive model. PLoS Comput Biol 8:e1002620PubMedCentralPubMedCrossRefGoogle Scholar
  15. Tanaka K, Khiroug L, Santamaria F, Doi T, Ogasawara H, Ellis-Davies GC, Kawato M, Augustine GJ (2007) Ca2+ requirements for cerebellar long-term synaptic depression: role for a postsynaptic leaky integrator. Neuron 54:787–800PubMedCrossRefGoogle Scholar
  16. Torrecillas A, Laynez J, Menéndez M, Corbalán-García S, Gómez-Fernández JC (2004) Calorimetric study of the interaction of the C2 domains of classical protein kinase C isoenzymes with Ca2+ and phospholipids. Biochemistry 43:11727–11739PubMedCrossRefGoogle Scholar

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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Molecular Neuroscience Department, Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA