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.