The brain is the source of thoughts, perceptions, emotions, memories and actions. Neural signaling, the foundation of brain activity, must be precisely regulated to prevent neuronal disorders that may cause Parkinson's disease, schizophrenia, compulsive behaviors and addiction. Such a precise regulation is achieved by key signaling proteins, voltage-gated sodium and potassium channels for electrical signaling and calcium - bound synaptotagmin for chemical signaling. Here, innovations in computer simulation techniques will be used to investigate the molecular mechanism of neural firing induced by voltage-gated sodium and potassium channels and membrane fusion triggered by synaptotagmin.

Spotlight: Better NAMD Electrostatics (May 2015)

Multilevel Summation Method in NAMD

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Long-range electrostatic interactions control macromolecular processes within living cells as prominent charges appear everywhere, such as in DNA or RNA, in membrane lipid head groups, and in ion channels. Reliable and efficient description of electrostatic interactions is crucial in molecular dynamics simulations of such processes. Recently a new mathematical approach for calculating electrostatic interactions, known as multilevel summation method (MSM), has been developed and programmed into NAMD 2.10 as reported here. Compared to the earlier decades-long approach, the particle-mesh Ewald (PME) method, MSM provides more flexibility as it permits non-periodic simulations like ones with asymmetric charge distributions across a membrane or of a water droplet with a protein folding inside. Furthermore, MSM is ideally suited for modern parallel computers, running, for example, simulations of large virus particles. More information here.

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  • Multilevel summation method for electrostatic force evaluation. David J. Hardy, Zhe Wu, James C. Phillips, John E. Stone, Robert D. Skeel, and Klaus Schulten. Journal of Chemical Theory and Computation, 11:766-779, 2015.
  • Synaptotagmin's role in neurotransmitter release likely involves Ca2+-induced conformational transition. Zhe Wu and Klaus Schulten. Biophysical Journal, 107:1156-1166, 2014.
  • A highly tilted membrane configuration for the pre-fusion state of synaptobrevin. Andrew E. Blanchard, Mark J. Arcario, Klaus Schulten, and Emad Tajkhorshid. Biophysical Journal, 107:2112-2121, 2014.
  • Structural mechanism of voltage-dependent gating in an isolated voltage-sensing domain. Qufei Li, Sherry Wanderling, Marcin Paduch, David Medovoy, Abhishek Singharoy, Ryan McGreevy, Carlos Villalba-Galea, Raymond E. Hulse, Benoit Roux, Klaus Schulten, Anthony Kossiakoff, and Eduardo Perozo. Nature Structural & Molecular Biology, 21:244-252, 2014.
  • Membrane sculpting by F-BAR domains studied by molecular dynamics simulations. Hang Yu and Klaus Schulten. PLoS Computational Biology, 9:e1002892, 2013. (15 pages).
  • Molecular dynamics investigation of the w current in the Kv1.2 voltage sensor domains. Fatemeh Khalili-Araghi, Emad Tajkhorshid, Benoit Roux, and Klaus Schulten. Biophysical Journal, 102:258-267, 2012.
  • Calculation of the gating charge for the Kv1.2 voltage-activated potassium channel. Fatemeh Khalili-Araghi, Vishwanath Jogini, Vladimir Yarov-Yarovoy, Emad Tajkhorshid, Benoit Roux, and Klaus Schulten. Biophysical Journal, 98:2189-2198, 2010.
  • Simulations of membrane tubulation by lattices of amphiphysin N-BAR domains. Ying Yin, Anton Arkhipov, and Klaus Schulten. Structure, 17:882-892, 2009.
  • Four-scale description of membrane sculpting by BAR domains. Anton Arkhipov, Ying Yin, and Klaus Schulten. Biophysical Journal, 95:2806-2821, 2008.
  • Dynamics of K+ ion conduction through Kv1.2. Fatemeh Khalili-Araghi, Emad Tajkhorshid, and Klaus Schulten. Biophysical Journal, 91:L72-L74, 2006.
  • Biological visuo-motor control of a pneumatic robot arm. Michael Zeller, K. R. Wallace, and Klaus Schulten. In Dagli et al., editors, Intelligent Engineering Systems Through Artificial Neural Networks, volume 5, pp. 645-650, New York, 1995. American Society of Mechanical Engineers.
  • A critical comparison of models for orientation and ocular dominance columns in the striate cortex. Edgar Erwin, Klaus Obermayer, and Klaus Schulten. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems 7, pp. 93-100. MIT Press, Cambridge, Mass and London, England, 1995.
  • Models of orientation and ocular dominance columns in the visual cortex: A critical comparison. Edgar Erwin, Klaus Obermayer, and Klaus Schulten. Neural Computation, 7:425-468, 1995.
  • A neural network for robot control: Cooperation between neural units as a requirement for learning. Thomas Martinetz and Klaus Schulten. Computers & Electrical Engineering, 19:315-332, 1993.
  • A neural network with Hebbian-like adaptation rules learning visuomotor coordination of a PUMA robot. Thomas Martinetz and Klaus Schulten. In Proceedings of the IEEE International Conference on Neural Networks (ICNN-93), San Francisco, pp. 820-825, 1993.
  • A comparison of models of visual cortical map formation. Edgar Erwin, Klaus Obermayer, and Klaus Schulten. In Frank H. Eeckman and James M. Bower, editors, Computation and Neural Systems, chapter 60, pp. 395-402. Kluwer Academic Publishers, 1993.
  • Industrial robot learns visuo-motor coordination by means of "neural gas" network. Jörg A. Walter, Thomas Martinetz, and Klaus Schulten. In Teuvo Kohonen, Kai Mäkisara, Olli Simula, and Jari Kangas, editors, Artificial Neural Networks, pp. 357-364. Elsevier, Amsterdam, 1991.
  • A "neural gas" network learns topologies. Thomas Martinetz and Klaus Schulten. In Teuvo Kohonen, Kai Mäkisara, Olli Simula, and Jari Kangas, editors, Artificial Neural Networks, pp. 397-402. Elsevier, Amsterdam, 1991.
  • Dynamics of synchronous neural activity in the visual cortex. Christian Kurrer, Benno Nieswand, and Klaus Schulten. In Teuvo Kohonen, Kai Mäkisara, Olli Simula, and Jari Kangas, editors, Artificial Neural Networks, pp. 133-138. Elsevier, Amsterdam, 1991.