A kinetic ensemble of the Alzheimer's Aβ peptide

The conformational and thermodynamic properties of disordered proteins are commonly described in terms of structural ensembles and free energy landscapes. To provide information on the transition rates between the different states populated by these proteins, it would be desirable to generalize this description to kinetic ensembles. Approaches based on the theory of stochastic processes can be particularly suitable for this purpose. Here, we develop a Markov state model and apply it to determine a kinetic ensemble of A beta 42, a disordered peptide associated with Alzheimer's disease. Through the Google Compute Engine, we generated 315-mu s all-atom molecular dynamics trajectories. Using a probabilistic-based definition of conformational states in a neural network approach, we found that A beta 42 is characterized by inter-state transitions on the microsecond timescale, exhibiting only fully unfolded or short-lived, partially folded states. Our results illustrate how kinetic ensembles provide effective information about the structure, thermodynamics and kinetics of disordered proteins.

Automated summary

The article discusses the conformational and thermodynamic properties of disordered proteins and proposes a method to describe the transition rates between different states using kinetic ensembles. By developing a Markov state model, the kinetic ensemble of A beta 42 is studied, revealing characteristics of state transitions on the microsecond timescale.