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Vibrationally resolved fluorescence spectra of the β-hairpin trpzip2 peptide at two temperatures as well as during a T-jump unfolding process are simulated on the basis of a combination of Markov state models and quantum chemistry schemes. The broad asymmetric spectral line shape feature is reproduced by considering the exciton-phonon couplings. The temperature dependent red shift observed in the experiment has been attributed to the state population changes of specific chromophores. Through further theoretical study, it is found that both the environment's electric field and the chromophores' geometry distortions are responsible for tryptophan fluorescence shift.

Fast folding techniques use optical spectroscopic tools to monitor protein folding or unfolding dynamics after a fast triggering such as the laser induced temperature jump. These techniques have greatly improved time resolution of experiments and provide new opportunities for comparison between theory and simulations. However, the direct comparison is still difficult due to two main challenges: a gap between folding relevant timescales (microseconds or above) and length of molecular dynamics simulations (typically tens to hundreds of nanoseconds), and difficulty in directly calculating spectroscopic observables from simulation configurations. This review is focused on recent advances in addressing these two challenges. We describe new methodology that allows simulating folding timescales with an emphasis on Markov State Models. We also review progress on modeling infrared, circular dichroism, and fluorescence spectroscopic signals from protein conformations. At last, we discuss a few studies that directly simulate time-resolved spectroscopy of temperature jump induced unfolding dynamics for a few small proteins. These studies not only provide direct validation of theoretical models, but also greatly improve our understanding of protein folding mechanisms by connecting ensemble averaged spectroscopic observables with atomistic protein conformations.

We proposed a computational protocol of simulating the T-jump peptide unfolding experiments and the related transient IR and two-dimensional IR (2DIR) spectra based on the Markov state model (MSM) and nonlinear exciton propagation (NEP) methods. MSMs partition the conformation space into a set of nonoverlapping metastable states, and we can calculate spectra signal for each of these states using the NEP method. Thus the overall spectroscopic observable for a given system is simply the sum of spectra of different metastable states weighted by their populations. We show that results from MSMs constructed from a large number of simulations have a much better agreement with the equilibrium experimental 2DIR spectra compared to that generated from straightforward MD simulations starting from the folded state. This indicates that a sufficient sampling of important relevant conformational states is critical for calculating the accurate spectroscopic observables. MSMs are also capable of simulating the unfolding relaxation dynamics upon the temperature jump. The agreement of the simulation using MSMs and NEP with the experiment not only provides a justification for our protocol, but also provides the physical insight of the underlying spectroscopic observables. The protocol we developed has the potential to be extended to simulate a wide range of fast triggering plus optical detection experiments for biomolecules.

Markov state models (MSMs) have become a popular approach for investigating the conformational dynamics of proteins and other biomolecules. MSMs are typically built from numerous molecular dynamics simulations by dividing the sampled configurations into a large number of microstates based on geometric criteria. The resulting microstate model can then be coarse-grained into a more understandable macrostate model by lumping together rapidly mixing microstates into larger, metastable aggregates. However, finite sampling often results in the creation of many poorly sampled microstates. During coarse-graining, these states are mistakenly identified as being kinetically important because transitions to/from them appear to be slow. In this paper, we propose a formalism based on an algebraic principle for matrix approximation, i.e., the Nystrom method, to deal with such poorly sampled microstates. Our scheme builds a hierarchy of microstates from high to low populations and progressively applies spectral clustering on sets of microstates within each level of the hierarchy. It helps spectral clustering identify metastable aggregates with highly populated microstates rather than being distracted by lowly populated states. We demonstrate the ability of this algorithm to discover the major metastable states on two model systems, the alanine dipeptide and trpzip2 peptide.