We have a large amount of simulation data from many related systems. Instead of selecting a different model-building approach for each system, we would like to select and automate a uniform MSM-building procedure, so that we can more easily compare MSMs.
Trajectories --> discrete trajectories --> statistics
msm-pipeline /cbio/jclab/projects/fah/fah-data/munged-with-time/no-solvent/11400/run0-clone0.h5
Default project_name
is 'abl', otherwise add project name in second argument:
msm-pipeline /cbio/jclab/projects/fah/fah-data/munged-with-time/no-solvent/11400/run0-clone0.h5 'abl'
- Inputs:
- List of filenames of munged hdf5 trajectories
- These are the outputs of the FAHmunge pipeline
- Lag-time between recorded snapshots
- Lag-time for MSM estimation
- Metastability lag-time
- List of filenames of munged hdf5 trajectories
- Indexing:
- Build an index of
filename : nframes
pairs. - Note that these files can be written to--
nframes
can increase over time-- can't assume fixed file length, but can assume that the only file modifications will be to append to the end of the trajectory.
- Build an index of
- Discretizing w.r.t. an appropriate metric
- Two typical choices of metric:
- Explicit-feature-based kinetic distance:
- Compute features (angles, inter-residue distances, etc.)
- Save computed features as a
.npz
archive
- Save computed features as a
- Use tICA to find a linear projection where Euclidean distance approximates diffusion distance
- Compute features (angles, inter-residue distances, etc.)
- MinRMSD
- Explicit-feature-based kinetic distance:
- Number of clusters. For now, we will fix this to be a large-ish number (1000), as we can recover from an overly fine discretization by "lumping" later.
- Choice of clustering algorithm: for now, fix to minibatch k-means.
- Save discrete trajectories as a
.npz
archive
- Two typical choices of metric:
- Statistics of discretized trajectories
- Distribution of trajectory lengths
- "Ergodic trimming" -- select the largest ergodic subspace, and estimate a Markov model
- Test Markovianity
- Implied timescales plots
- Chapman-Kolmogorov tests among macrostates (macrostates will be defined below!)
- "Lump" microstates into macrostates
- Using eigenvalues of transition matrix
- What do we do for "disconnected" models?
- In cases where the observed dynamics are not ergodic, what should we do?
- What scheme gives the best model?