Bridging Experiments and Native-Centric Simulations of a Downhill Folding Protein

Experiments and atomistic simulations have independently contributed to the mechanistic understanding of protein folding. However, a coherent detailed picture explicitly combining both is currently lacking, a problem that seriously limits the amount of information that can be extracted. An alternative to atomistic models with physics-based potentials is the native-centric (i.e., G (o) over bar type) coarse-grained models, which for many years have been successfully employed to qualitatively understand features of protein folding energy landscapes. Again, quantitative validation of G (o) over bar models against experimental equilibrium unfolding curves is often not attempted. Here we use an atomistic topology-based model to study the folding mechanism of PDD, a protein that folds over a marginal thermodynamic barrier of similar to 0.5 k(B)T at midpoint conditions. We find that the simulations are in exquisite agreement with several equilibrium experimental measurements including differential scanning calorimetry (DSC), an observable that is possibly the most challenging to reproduce from explicit-chain models. The dynamics, inferred using a detailed Markov state model, display a classical Chevron-like trend with a continuum of relaxation times under both folding and unfolding conditions, a signature feature of downhill folding. The number of populated microstates and the connectivity between them are shown to be temperature dependent with a maximum near the thermal denaturation midpoint, thus linking the macroscopic observation of a peak in the DSC profile of downhill folding proteins and the underlying microstate dynamics. The mechanistic picture derived from our analysis thus sheds light on the intricate and tunable nature of the downhill protein folding ensembles. In parallel, our work highlights the power of coarse-grained models to reproduce experiments at a quantitative level while also pointing at specific directions for their improvement.