Folding Forum

I have several questions about F@h's simulation methods (Markov state models). I think I have a general understanding of how they work, but for various reasons I'd like to learn a bit more. I'm hoping someone could provide some insights.

• How are the local minima in the energy landscape from which the simulations are started found? Why is this not computationally difficult?
• In what way do MSM's explore the protein's phase space differently than normal simulation techniques which don't avoid simulating the "waiting" inside the energy minima?
• How do you know that the simulation will reach a minima, and how is the simulation direction chosen?
• Is this approach adequate for exploring the protein's phase space? If it does such a thorough job and gives you so much conformation and kinetic information why are really long simulations so important? Dr. Pande has said several times that specialized systems like Anton would be useful to F@h and I'm just wondering why because it only performs a few long simulations.
• I've read that protein's don't always stay in their folded shape. Does F@h simulate this refolding, and how does that work with MSMs?

I'm still trying to figure out how to access the relevant publications outside of a university network. In the meantime, I'm hoping someone can help me understand these things further. I understand that these are technical questions. If you know some of the answers, I'd sure appreciate it, even if it's easiest for you to use technical terms if need be. Thanks.

Check out this paper – it should answer these sorts of questions and is now available for free (see link below).

Everything you wanted to know about Markov State Models but were afraid to ask.
Pande VS, Beauchamp K, Bowman GR.
Program in Biophysics, Stanford University, USA. pande@stanford.edu

Abstract
Simulating protein folding has been a challenging problem for decades due to the long timescales involved (compared with what is possible to simulate) and the challenges of gaining insight from the complex nature of the resulting simulation data. Markov State Models (MSMs) present a means to tackle both of these challenges, yielding simulations on experimentally relevant timescales, statistical significance, and coarse grained representations that are readily humanly understandable. Here, we review this method with the intended audience of non-experts, in order to introduce the method to a broader audience. We review the motivations, methods, and caveats of MSMs, as well as some recent highlights of applications of the method. We conclude by discussing how this approach is part of a paradigm shift in how one uses simulations, away from anecdotal single-trajectory approaches to a more comprehensive statistical approach.

http://www.ncbi.nlm.nih.gov/pubmed/20570730

VijayPande wrote:Check out this paper – it should answer these sorts of questions and is now available for free (see link below). ...

Thanks. Glad to see that it's not super technical and I think I can read it pretty well.

Updated paper 85 with link to free pub.

Also curious about this statement, especially when you are attending a university... "I'm still trying to figure out how to access the relevant publications outside of a university network."

Ironic title...


Jesse, have you seen this information about locating scientific publications? Resources for Finding and Accessing Scientific Papers It includes several alternate methods for finding free copies of pubs.