Quantitative views of protein dynamics through the computational microscope.

Abstract

The complexity of the energy landscape of proteins and subjectivity in choosing reaction coordinates pose significant hurdles in the quantitative characterization of functional protein dynamics using computational techniques. We have proposed the cumulative variance of coordinate fluctuations (CVCF) as a parameter free and energy landscape sensitive metric to identify sections of Molecular dynamics (MD) and Monte-Carlo (MC) trajectories which achieve Boltzmann sampling conditions. Our CVCF-trace analysis can provide quantitative estimates of the effective curvature and roughness of the energy landscape of proteins. We have applied these conceptual advances to compare the overall and directional flexibilities of multiple protein systems in monomeric form as well as within complexes. The results not only correlate well with existing experimental single molecule force spectroscopy data but also provide clear predictions which can be tested by such experiments. Moving beyond CVCF, we propose a mode evolution metric (MEM) to track the evolution of protein dynamics along MD trajectories. MEM enables us to deduce the dimensionality of protein motions dictated by the underlying energy landscape, thereby providing an objective means to identify reaction coordinates to describe dynamics. We will discuss improved biomolecular energy landscape sampling formulations based on our set of dynamical metrics.