Guest post from Dr. Greg Bowman, University of California, Berkeley

Markov state models

(MSMs) are a powerful approach for investigating the dynamics of proteins and

other biomolecules. The Folding@home team has helped to pioneer the development

of these methods and continues to make important contributions to their further

development. For example, the Huang lab

at the Hong Kong University of Science and Technology has created an exciting

new method for coarse-graining Markov models (the paper is available here).

One of the major

challenges in this area is that the high-resolution Markov models capable of

making quantitative predictions of experiments often have tens of thousands of

parts. As you can imagine, it is hard to

look at each of these parts and the interactions between them to understand the

model. Therefore, it is valuable to

create a new model with, say, a dozen states that still captures much of the

behavior of the more complex model.

The new method from

the Huang lab makes use of a mathematical principle called a Nystrom expansion

to build more accurate coarse-grained models.

The key advantage of this approach is that one can quickly identify the

most important pieces of a model and prove that the remaining pieces can safely

be ignored (or merged into the more important parts). As a proof of principle, the Huang lab has

shown they can build much better models for a few small proteins than is

possible with previous methods.