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.
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
are being developed.
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,
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
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
More on our
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 "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.