Command Line Interface (CLI)¶

MSMBuilder is really designed (at this point) as a python library. It’s much more powerful that way, and not any less convenient (especially when you’re deailing with large datasets). But there is a limited CLI as well.

Tutorial¶

The command line tool is msmb. Read the help options:
```
msmb -h
```
In your own research, you probably have a large molecular dynamics dataset that you wish to analyze. For this tutorial, we will perform a quick analysis on a very simple system: alanine-dipeptide.Make a working directory and get an example dataset:
```
mkdir ~/msmb_tutorial
cd ~/msmb_tutorial
msmb AlanineDipeptide --data_home ./
ls alanine_dipeptide/
```

You should see 10 .dcd trajectory files and an ala2.pdb topology file.

First, we need to turn the time-series of atomic coordinates we get from MD into useable “features”. There are many choices of relevant features. We’ll use pairwise distances between heavy atoms. First, we need to select the heavy atoms:
```
msmb AtomIndices -d --heavy -p alanine_dipeptide/ala2.pdb -o AtomIndices.dat
```
Read the help text to make sure you understand each of these options.
Now we can compute the pairwise distances for each frame:
```
msmb AtomPairsFeaturizer --out pairs.jl --pair_indices AtomIndices.dat \
    --top alanine_dipeptide/ala2.pdb --trjs "alanine_dipeptide/*.dcd"
```
We give the topology file, a ‘glob’ expression to all of our trajectories, the atom indices we generated in the previous step, and an output filename.
We can train a kinetic model like tICA to transform our input features (atom pair distances) into the most kinetically relevant linear combinations of those features:
```
msmb tICA --inp pairs.jl --out tica_model.jl --transformed tica_trajs.jl \
    --n_components 4
```

This code takes our feature trajectories stored in pairs.jl, fits a tICA model (saved as tica_model.jl) and transforms our feature trajectories into new trajectories (tica_trajs.jl). We specify that we want the top 4 slowest-components.

We can plot a 2d histogram of the top two tICA coordinates

 import msmbuilder.utils
 from matplotlib import pyplot as plt
 import numpy as np

 trajs = msmbuilder.utils.verboseload('tica_trajs.jl')  # Load file
 trajs = np.concatenate(trajs)  # Flatten list of trajectories
 plt.hexbin(trajs[:,0], trajs[:,1], bins='log', mincnt=1)
 plt.show()

And run with python::

 python plot_tica.py