  State-free reversible VAMPnets (SRVs) are a neural network-based framework
capable of learning the leading eigenfunctions of the transfer operator of a
dynamical system from trajectory data. In molecular dynamics simulations, these
data-driven collective variables (CVs) capture the slowest modes of the
dynamics and are useful for enhanced sampling and free energy estimation. In
this work, we employ SRV coordinates as a feature set for Markov state model
(MSM) construction. Compared to the current state of the art, MSMs constructed
from SRV coordinates are more robust to the choice of input features, exhibit
faster implied timescale convergence, and permit the use of shorter lagtimes to
construct higher kinetic resolution models. We apply this methodology to study
the folding kinetics and conformational landscape of the Trp-cage miniprotein.
Folding and unfolding mean first passage times are in good agreement with prior
literature, and a nine macrostate model is presented. The unfolded ensemble
comprises a central kinetic hub with interconversions to several metastable
unfolded conformations and which serves as the gateway to the folded ensemble.
The folded ensemble comprises the native state, a partially unfolded
intermediate "loop" state, and a previously unreported short-lived intermediate
that we were able to resolve due to the high time-resolution of the SRV-MSM. We
propose SRVs as an excellent candidate for integration into modern MSM
construction pipelines.
