2011/10/11 by Thomas J. Lane, Gregory R. Bowman, Kyle A. Beauchamp +2 · 2 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Mathematics · #Protein Structure and Dynamics #RNA and protein synthesis mechanisms #Mass Spectrometry Techniques and Applications #Folding (DSP implementation) #Markov chain #Chemistry #Statistical physics #Domain (mathematical analysis) #Markov model #Projection (relational algebra) #Biological system #Markov process #Molecular dynamics #WW domain #Computational biology #Algorithm #Computational chemistry #Computer science #Physics #Machine learning #Statistics #Mathematics
paper · doi:10.1021/ja207470h
openalex publication_date 2011/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Two strategies have been recently employed to push molecular simulation to long, biologically relevant time scales: projection-based analysis of results from specialized hardware producing a small number of ultralong trajectories and the statistical interpretation of massive parallel sampling performed with Markov state models (MSMs). Here, we assess the MSM as an analysis method by constructing a Markov model from ultralong trajectories, specifically two previously reported 100 μs trajectories of the FiP35 WW domain (Shaw, D. E. Science 2010, 330, 341-346). We find that the MSM approach yields novel insights. It discovers new statistically significant folding pathways, in which either beta-hairpin of the WW domain can form first. The rates of this process approach experimental values in a direct quantitative comparison (time scales of 5.0 μs and 100 ns), within a factor of ∼2. Finally, the hub-like topology of the MSM and identification of a holo conformation predicts how WW domains may function through a conformational selection mechanism.