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MSM & kinetic seeding

Trails-MD separates adaptive exploration from kinetic estimation. Walkers are short, and their velocities are redrawn from a Maxwell-Boltzmann distribution at each spawn point, so adaptive trajectories are designed for efficient exploration of a CV space, not as an unbiased kinetic ensemble on their own.

Scope note. The two-stage strategy below (adaptive exploration → seed → post-hoc MSM) is the manuscript-supported workflow. The optional in-loop MSM (msm.enabled: true, and MSM-driven convergence / spawning) is experimental/beta and outside the manuscript scope: it estimates an unweighted MSM on short, non-equilibrium, adaptively-selected walkers, so its rates are not unbiased. For quantitative kinetics, seed longer unbiased runs and reweight with TRAM/dTRAM rather than relying on the in-loop estimate.

The two-stage strategy

  1. Adaptive exploration. Run a Trails-MD campaign (see Concepts) to discover conformational space and identify representative structures across the explored region.
  2. Kinetic seeding. Use representative structures selected from the adaptive campaign to seed longer, unbiased production trajectories.
  3. Post-hoc MSM construction. Build a Markov State Model from the production trajectories using standard external tools (e.g. deeptime) — clustering, transition-matrix estimation, implied-timescale analysis, and coarse-graining into metastable states.

This two-stage approach is useful because the adaptive stage identifies representative starting structures across the explored space, while the production stage generates trajectories that are directly suitable for MSM estimation — avoiding the bias that would come from building a kinetic model directly on short, velocity-randomized adaptive walkers.

What Trails-MD provides for this workflow

  • Coverage diagnostics (trails-md-log) to identify well- and under-sampled regions of the CV space at the end of a campaign.
  • Lineage tracking (trails-md-path) to reconstruct connected parent-child trajectories, useful for selecting seeding structures along a hypothesized transition path.
  • Checkpointed campaign state, so representative-structure selection can be revisited without rerunning the adaptive stage.

Example: chignolin folding

The paper demonstrates this workflow on chignolin (CLN025) folding in explicit water: an adaptive Trails-MD campaign explores the CV space, representative spawn points are selected from the discretized explored space, and long production trajectories seeded from those points are used to build a two-state coarse-grained MSM with implied-timescale validation. See Results in the paper.