Choice of MSM lag time. MSM implied timescales were calculated as a function lag time, for all force fields, with uncertainties calculated using Bayesian sampling of the posterior error (Figure ). Beyond 20 ns, the slowest implied timescales plateau, becoming independent of the selected lag time for most force fields. To further validate this result, we performed additional cross-validation studies in which we computed average implied timescales for random bootstraps of the input data containing only ∼50% of trajectories (Figure ). The implied timescales versus lag time show similar trends, but with some force fields showing an increase in the uncertainty of the slowest timescale for lag times beyond 20 ns. Futhermore, attempts to construct models using lag times of 50 ns and 100 ns were unsuccessful for AMBER99 and AMBER99SB, due to a failure to consistently produce connected models that were robust to trajectory cross-validation. For these reasons, a MSM ability to accurately predict folded state populations, we sought a protocol to coarse-grain the trajectory data into folded, unfolded, and misfolded macrostates. Initial attempts to coarse-grain MSM microstates using the Robust Perron Cluster Analysis (PCCA+) spectral clustering algorithm resulted in inconsistent definitions across force fields, making comparison difficult. Therefore, we developed a structure-based classifier to consistently separate the three desired macrostates, native (N), misfolded (M) and unfolded (U), regardless of the force field. The classifier is based on structural differences between the native and misfolded states of chignolin using Asp3N-Gly7O and Asp3N-Thr8O atomic distances, which shows separation of these states in all force fields (Figure ). This distance space offers some ability to separate the target states, but defining specific cutoffs was challenging. To enhance macrostate separation, we therefore transformed these distances, d, using the function f (d) = e -βd /(1 + e -βd ) where β is a tuning parameter. Applying this function with β = 5 nm -1 generated large separation between the native and the misfolded state (Figure ). An arbitrary cutoff of f = 0.10 for each distance was applied to separate the native/unfolded and misfolded/unfolded regions, enabling the assignment of specific microstates to their corresponding macrostate according to the location of their k-means cluster center position. To assess the sensitivity of the macrostate assignments to this choice of cutoff, we investigated the macrostate determined using cutoffs of 0.075 and 0.125 (Figure ). The impact of changing the cutoff is minimal, which suggests that our choice of cutoff is reasonable.