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

High-resolution Markov state models for the dynamics of Trp-cage  miniprotein constructed over slow folding modes identified by state-free  reversible VAMPnets