Supercomputer Unlocks Secrets of the Brain and Safer Anesthetics

Summary: A new study reveals a switching mechanism that could help in the design of new anesthetic drugs.

Source: RMIT University.

Step towards safer and more effective use of drugs.

Researchers have used a supercomputer to show how proteins in the brain control electrical signals, in a breakthrough that could lead to safer and more effective drugs and anaesthetics.

In the seven-year study just released, RMIT University researchers in Melbourne, Australia – led by Professor Toby Allen and including Dr Bogdan Lev and Dr Brett Cromer – modelled how protein “switches” are activated by binding molecules to generate electrical signals in the brain.

The findings, which involved hundreds of millions of computer processing hours, pave the way for understanding how brain activity can be controlled by existing and new drugs, including anaesthetics.

General anaesthetics work by blocking “on” switches and enhancing “off” switches in the brain, leading to loss of sensation and the ability to feel pain.

“Even though anaesthetics have been used for more than 150 years, scientists still don’t know how they work at the molecular level,” Allen said.

“General anaesthetics are a mainstay of modern medicine, but have a small safety margin, requiring skilled anaesthetists for their safe use. They may also have long-term effects on brain function in both newborns and the elderly.

“Our study has uncovered details of the switching mechanism that will help in the design of new anaesthetics that are safer, both immediately and for long-term brain function, as well as more effective and more targeted use of anaesthetics.”

Allen said the computer models, using the Victorian Life Sciences Computation Initiative, provided an unprecedented level of understanding of the nervous system.

“These protein switches, called ligand-gated ion channels, are primary electrical components of our nervous systems. Understanding how they work is one of the most important questions in biology,” he said.

“Our computer models show something that’s never been seen before. We have discovered how ion channels bind molecules, such as neurotransmitters, and are activated to generate electrical signals in neurons.

Image shows a brain.
The findings also unlock a range of other potential applications including understanding how ion channel mutations cause diseases like epilepsy and startle disease, as well as new treatments for anxiety, alcoholism, chronic pain, stroke and other neural conditions. NeuroscienceNews.com image is for illustrative purposes only.

“We are now using these models to make important predictions for how the binding of drugs and anaesthetics may control electrical signalling.”

The findings also unlock a range of other potential applications including understanding how ion channel mutations cause diseases like epilepsy and startle disease, as well as new treatments for anxiety, alcoholism, chronic pain, stroke and other neural conditions.

And because all living organisms share similar proteins, the findings could also open up possibilities for safer and more effective insecticides and anti-parasitics, while the computer modelling developed in the study reduces the need to test new drugs on animals.

About this neuroscience research article

Funding: The study was funded by the National Health and Medical Research Council, as well as the Medical Advances Without Animals Trust.

Source: Toby Allen – RMIT University
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Abstract for “String method solution of the gating pathways for a pentameric ligand-gated ion channel” by Bogdan Lev, Samuel Murail, Frédéric Poitevin, Brett A. Cromer, Marc Baaden, Marc Delarue, and Toby W. Allen in PNAS. Published online May 9 2017 doi:10.1073/pnas.1617567114

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[cbtabs][cbtab title=”MLA”]RMIT University “Supercomputer Unlocks Secrets of the Brain and Safer Anesthetics.” NeuroscienceNews. NeuroscienceNews, 22 May 2017.
<https://neurosciencenews.com/brain-anesthetic-supercomputer-6750/>.[/cbtab][cbtab title=”APA”]RMIT University (2017, May 22). Supercomputer Unlocks Secrets of the Brain and Safer Anesthetics. NeuroscienceNew. Retrieved May 22, 2017 from https://neurosciencenews.com/brain-anesthetic-supercomputer-6750/[/cbtab][cbtab title=”Chicago”]RMIT University “Supercomputer Unlocks Secrets of the Brain and Safer Anesthetics.” https://neurosciencenews.com/brain-anesthetic-supercomputer-6750/ (accessed May 22, 2017).[/cbtab][/cbtabs]


Abstract

String method solution of the gating pathways for a pentameric ligand-gated ion channel

Pentameric ligand-gated ion channels control synaptic neurotransmission by converting chemical signals into electrical signals. Agonist binding leads to rapid signal transduction via an allosteric mechanism, where global protein conformational changes open a pore across the nerve cell membrane. We use all-atom molecular dynamics with a swarm-based string method to solve for the minimum free-energy gating pathways of the proton-activated bacterial GLIC channel. We describe stable wetted/open and dewetted/closed states, and uncover conformational changes in the agonist-binding extracellular domain, ion-conducting transmembrane domain, and gating interface that control communication between these domains. Transition analysis is used to compute free-energy surfaces that suggest allosteric pathways; stabilization with pH; and intermediates, including states that facilitate channel closing in the presence of an agonist. We describe a switching mechanism that senses proton binding by marked reorganization of subunit interface, altering the packing of β-sheets to induce changes that lead to asynchronous pore-lining M2 helix movements. These results provide molecular details of GLIC gating and insight into the allosteric mechanisms for the superfamily of pentameric ligand-gated channels.

“String method solution of the gating pathways for a pentameric ligand-gated ion channel” by Bogdan Lev, Samuel Murail, Frédéric Poitevin, Brett A. Cromer, Marc Baaden, Marc Delarue, and Toby W. Allen in PNAS. Published online May 9 2017 doi:10.1073/pnas.1617567114

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