Unfolded protein ensembles, folding trajectories, and refolding rate prediction

Abstract

Computer simulations can provide critical information on the unfolded ensemble of proteins under physiological conditions, by explicitly characterizing the geometrical properties of the diverse conformations that are sampled in the unfolded state. A general computational analysis across many proteins has not been implemented however. Here, we develop a method for generating a diverse conformational ensemble, to characterize properties of the unfolded states of intrinsically disordered or intrinsically folded proteins. The method allows unfolded proteins to retain disulfide bonds. We examined physical properties of the unfolded ensembles of several proteins, including chemical shifts, clustering properties, and scaling exponents for the radius of gyration with polymer length. A problem relating simulated and experimental residual dipolar couplings is discussed. We apply our generated ensembles to the problem of folding kinetics, by examining whether the ensembles of some proteins are closer geometrically to their folded structures than others. We find that for a randomly selected dataset of 15 non-homologous 2- and 3-state proteins, quantities such as the average root mean squared deviation between the folded structure and unfolded ensemble correlate with folding rates as strongly as absolute contact order. We introduce a new order parameter that measures the distance travelled per residue, which naturally partitions into a smooth "laminar" and subsequent "turbulent" part of the trajectory. This latter conceptually simple measure with no fitting parameters predicts folding rates in 0 M denaturant with remarkable accuracy (r = -0.95, p = 1 × 10(-7)). The high correlation between folding times and sterically modulated, reconfigurational motion supports the rapid collapse of proteins prior to the transition state as a generic feature in the folding of both two-state and multi-state proteins. This method for generating unfolded ensembles provides a powerful approach to address various questions in protein evolution, misfolding and aggregation, transient structures, and molten globule and disordered protein phases.