Folding@home Alzheimer's Simulation work published

I am very happy to announce that a first key work (paper #58 at from the Folding@home project on Alzheimer's Disease (AD) was just published

Simulating oligomerization at experimental concentrations and long timescales: A Markov state model approach, by Nicholas W. Kelley, V. Vishal, Grant A. Krafft, and Vijay S. Pande.   J. Chem. Phys. 129, 214707 (2008); DOI:10.1063/1.3010881 


Abeta misfolding and aggregation is believed to be the cause of Alzheimer's Disease. Simulations, like Folding@home, are a natural way to understand this process. However, there are several key challenges for simulating the key step — oligomerization.
This work represents a new way to simulate Abeta oligomerization, with a key advance of being able to simulate experimentally relevant timescales and concentrations, using a novel method. We use this new method and the power provided by Folding@home donors to simulate oligomerization in all-atom detail. This has lead to specific predictions about the process, which we are now testing experimentally. 

In many ways, this paper is the "tip of the iceberg" for the Folding@home activities in AD, with a lot more interesting results to come, especially in terms of experimental tests of our predictions and interesting new possibilities for new drugs and AD therapeutics.  So, while we're excited that this result is now past peer review, we're even more excited for what's coming down the pipeline, waiting peer review.  We'll keep you posted as more results become public, hopefully with some even bigger announcements in 2009.

It was asked which clients participated.  This work started several years ago and took some time to analyze and then publish.  So, it ran exclusively on classic clients.  For the follow up simulations, we are using a mixture of GPU, SMP, and classic clients.  Due to the large number of classic clients, they allow us to calculations not possible on the other platforms.  However, the raw speed (but smaller number) of the GPU and SMP clients allow us to get a good rough idea quickly, refining later with classic clients.