High-resolution Markov state models for the dynamics of Trp-cage miniprotein constructed over slow folding modes identified by state-free reversible VAMPnets
This work addresses the challenge of accurately modeling protein folding kinetics for computational biophysics, though it is incremental as it builds on existing SRV and MSM methods.
The authors tackled the problem of constructing high-resolution Markov state models (MSMs) for protein folding dynamics by using state-free reversible VAMPnets (SRVs) as features, resulting in more robust models with faster implied timescale convergence and the ability to resolve a previously unreported short-lived intermediate in the Trp-cage miniprotein.
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.