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: xMDFF Enhances X-Ray Structures (Aug 2014)

xMDFF

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For many, the word 'X-ray' conjures up the images of white bones on black backgrounds hanging on the wall of a doctor's office. However, X-rays have played another important role for the past 100 years through their use in the determination of chemical structures at atomic level detail, starting with the first ever structure of table salt in 1924. Since then, the diffraction properties of X-rays, when shone on a crystal, have been used to solve increasingly large and complex structures including those of biological macromolecules found inside living cells. X-ray crystallography has become the most versatile and dominant technique for determining atomic structures of biomolecules, but despite its strengths, X-ray crystallography struggles in the case of large or flexible structures as well as in the case of membrane proteins, either of which diffract only at low resolutions. Because solving structures from low-resolution data is a difficult, time-consuming process, such data sets are often discarded. To face the challenges posed by low-resolution, new methods, such as xMDFF (Molecular Dynamics Flexible Fitting for X-ray Crystallography) described here, are being developed. xMDFF extends the popular MDFF software originally created for determining atomic-resolution structures from cryo-electron microscopy density maps (see the previous highlights Seeing Molecular Machines in Action, Open Sesame, Placing New Proteins, and Elusive HIV-1 Capsid). xMDFF provides a relatively easy solution to the difficult process of refining structures from low-resolution data. The method has been successfully applied to experimental data as described in a recent article where xMDFF refinement is explained in detail and its use is demonstrated. Together with electrophysiology experiments, xMDFF was also used to validate the first all-atom structure of the voltage sensing protein Ci-VSP, as also recently reported. More on our MDFF website.

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  • 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.
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  • 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.
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  • 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.
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