Characterizing global substates of myoglobin

The massive amount of information generated from current molecular dynamics simulations makes the data difficult to analyze efficiently. Principal component analysis has been used for almost a century to detect and characterize data relationships and to reduce the dimensionality for problems in many fields. Here, we present an adaptation of principal component analysis using a partial singular value decomposition (SVD) for investigating both the localized and global motions of macromolecules.

Configuration space projections from the SVD analysis of a variety of myoglobin simulations are used to characterize the dynamics of the protein. This technique reveals new dynamical motifs, which quantify proposed hierarchical structures of conformational substates for proteins and provide a means by which configuration space sampling efficiency may be probed. The SVD clearly shows that solvent effects facilitate transitions between global conformational substates for myoglobin molecular dynamics simulations. Lyapunov exponents calculated from the configuration space divergence of 15 trajectories agree with previous predictions for the chaotic behavior of complex protein systems.

Configuration space projections provide invaluable information about protein motions that would be extremely difficult to obtain otherwise. While the configuration space for myoglobin is quite large, it does have structure. Our analysis of this structure shows that the protein hops between a number of distinct global conformational states, much like the local behavior observed for an individual residue.