Simulating oligomerization at experimental concentrations and long timescales: A Markov state model approach

Nicholas W. Kelley, V. Vishal, Grant A. Krafft, and Vijay S. Pande

J. Chem. Phys. 129, 214707 (2008); DOI:10.1063/1.3010881

SUMMARY. 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.

This work 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.

ABSTRACT. Here, we present a novel computational approach for describing the formation of oligomeric assemblies at experimental concentrations and timescales. We propose an extension to the Markovian state model approach, where one includes low concentration oligomeric states analytically. This allows simulation on long timescales (seconds timescale) and at arbitrarily low concentrations (e.g., the micromolar concentrations found in experiments), while still using an all-atom model for protein and solvent. As a proof of concept, we apply this methodology to the oligomerization of an Abeta peptide fragment (Abeta 21–43). Abeta oligomers are now widely recognized as the primary neurotoxic structures leading to Alzheimer’s disease. Our computational methods predict that Abeta trimers form at micromolar concentrations in 10 ms, while tetramers form 1000 times more slowly. Moreover, the simulation results predict specific intermonomer contacts present in the oligomer ensemble as well as putative structures for small molecular weight oligomers. Based on our simulations and statistical models, we propose a novel mutation to stabilize the trimeric form of Abeta in an experimentally verifiable manner.