# MSMBuilder Commands¶

The MSMBuilder commands are listed below. Each command corresponds to a single scrip that can be called either through msmb or on its own. Each command also provides instructions by running with the -h flag (e.g. msmb Cluster -h or Cluster.py -h). Note that the installer (setup.py) should have installed each script below to someplace in your PATH.

## msmb¶

All of the individual msmbuilder commands can be accessed as subcommands from this script, i.e. msmb ConvertDataToHDF. Using msmb -h, you can get a list of all of the available msmbuilder commands.

## ConvertDataToHDF.py¶

Merges sequences of XTC or DCD files into HDF5 files that MSMB2 can read quickly. Takes data from a directory of trajectory directories or a FAH-style filesystem.

## CreateAtomIndices.py¶

Selects atom indices you care about and dumps them into a flat text file. Can select all non-symmetric atoms, all heavy atoms, all alpha carbons, or all atoms.

## Cluster.py¶

Cluster your data using your choice of clustering algorithm and distance metric. We have previously used several clustering protocols, which are summarized:

1. RMSD + k-centersCluster.py rmsd )
2. RMSD + hybrid k-centers / k-medoidsCluster.py rmsd )
3. RMSD + WardCluster.py rmsd )

Note that Ward clustering calculates an $$O(N^2)$$ distance matrix, which may be prohibitive for datasets with many conformations.

Most of our experience has been in applying MSMBuilder to protein folding. Thus, non-folding applications may require a slightly different protocol.

## Assign.py / AssignHierarchical.py¶

Assign.py assigns data to the cluster generators calculated using the k-centers or hybrid algorithms.

AssignHierarchical.py assigns data using the output of a hierarchical clustering algorithm such as Ward. The key difference is that a single hierarchical clustering allows construction of models with any number of states.

## CalculateImpliedTimescales.py¶

Calculates the implied timescales for a python range of MSM lag times. This allows you to validate whether a given model is Markovian. Notes:

1. You might get a SparseEfficiencyWarning for every lag time. Ignore this.
2. Lagtimes are input in units of the time spacing between successive trajectory frames. If your trajectories are stored every 10 ns, then -l 1,4 estimates implied timescales with lagtimes 10, 20, 30, 40 ns.

## PlotImpliedTimescales.py¶

A template for generating an implied timescales plot.

## BuildMSM.py¶

Estimate a reversible transition and count matrix using a two step process:

1. Use Tarjan algorithm to find the maximal strongly-connected (ergodic) subgraph
2. Use likelihood maximization to estimate a reversible count matrix consistent with your data

This script also outputs the equilibrium populations of the resulting model, as well as a mapping from the original states to the final (ergodic) states.

## GetRandomConfs.py¶

Selects random conformations from each state of your MSM. This is very useful for efficient calculation of observables.

Calculates the mean RMSD of all assigned snapshots to their cluster generator for each cluster. Gives an indication of how structurally diverse clusters are.

## CalculateRMSD.py¶

Calculate the RMSD between a PDB and a trajectory (or set of cluster centers). Useful for deciding which clusters belong to the folded, unfolded, or transition state ensembles (or any other grouping!)

## CalculateProjectRMSD.py¶

Calculates the RMSD of all conformations in a project to a given conformation.

## CalculateTPT.py¶

Performs Transition Path Theory (TPT) calculations. You will need to define good starting (reactants/U) and ending (products/F) ensembles for this script. Writes the forward and backward committors and the net flux matrix

## SavePDBs.py¶

Allows you to sample random PDBs from particular states and save them to disk.

## PCCA.py¶

Lumps microstates into macrostates using PCCA or PCCA+ . This script generates a macrostate assignments file from a microstate model.

Notes:

1. We recommend PCCA+ for most applications
2. PCCA+ requires a reversible MSM as input
3. You can discard eigenvectors based on their equilibrium flux (fPCCA+).

## BACE_Coarse_Graining.py¶

An alternative method for lumping microstates into macrostates using a Bayesian approach (Bayesian agglomerative clustering engine) . This is an attractive option as it appears to outperform existing spectral methods. To learn how to use BACE, run the script with the -h option.

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