Femtosecond time-resolved spectroscopy on model peptides with built-in light switches combined with computer simulation of light-triggered motions offers an attractive integrated approach toward the understanding of peptide conformational dynamics. It was applied to monitor the light-induced relaxation dynamics occurring on subnanosecond time scales in a peptide that was backbone-cyclized with an azobenzene derivative as optical switch and spectroscopic probe. The femtosecond spectra permit the clear distinguishing and characterization of the subpicosecond photoisomerization of the chromophore, the subsequent dissipation of vibrational energy, and the subnanosecond conformational relaxation of the peptide. The photochemical cis͞trans-isomerization of the chromophore and the resulting peptide relaxations have been simulated with molecular dynamics calculations. The calculated reaction kinetics, as monitored by the energy content of the peptide, were found to match the spectroscopic data. Thus we verify that all-atom molecular dynamics simulations can quantitatively describe the subnanosecond conformational dynamics of peptides, strengthening confidence in corresponding predictions for longer time scales.

A high-dimensional time series obtained by simulating a complex and stochastic dynamical system (like a peptide in solution) may code an underlying multiple-state Markov process. We present a computational approach to most plausibly identify and reconstruct this process from the simulated trajectory. Using a mixture of normal distributions we first construct a maximum likelihood estimate of the point density associated with this time series and thus obtain a density-oriented partition of the data space. This discretization allows us to estimate the transfer operator as a matrix of moderate dimension at sufficient statistics. A nonlinear dynamics involving that matrix and, alternatively, a deterministic coarse-graining procedure are employed to construct respective hierarchies of Markov models, from which the model most plausibly mapping the generating stochastic process is selected by consideration of certain observables. Within both procedures the data are classified in terms of prototypical points, the conformations, marking the various Markov states. As a typical example, the approach is applied to analyze the conformational dynamics of a tripeptide in solution. The corresponding high-dimensional time series has been obtained from an extended molecular dynamics simulation.

The affinity and selectivity of protein-protein interactions can be fine-tuned by varying the size, flexibility, and amino acid composition of involved surface loops. As a model for such surface loops, we study the conformational landscape of an octapeptide, whose flexibility is chemically steered by a covalent ring closure integrating an azobenzene dye into and by a disulfide bridge additionally constraining the peptide backbone. Because the covalently integrated azobenzene dyes can be switched by light between a bent cis state and an elongated trans state, six cyclic peptide models of strongly different flexibilities are obtained. The conformational states of these peptide models are sampled by NMR and by unconstrained molecular dynamics (MD) simulations. Prototypical conformations and the free-energy landscapes in the high-dimensional space spanned by the phi/psi angles at the peptide backbone are obtained by clustering techniques from the MD trajectories. Multiple open-loop conformations are shown to be predicted by MD particularly in the very flexible cases and are shown to comply with the NMR data despite the fact that such open-loop conformations are missing in the refined NMR structures.