Performing Hausdorff pairs analysis using PSA

In this example, the trajectories have been pre-aligned (as in and psa_short.ipynb) using the fitting scheme described in:

S.L. Seyler, A. Kumar, M.F. Thorpe, and O. Beckstein, Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways. arXiv:1505.04807v1_ [q-bio.QM], 2015.

In [1]:
%matplotlib inline
%load_ext autoreload
%autoreload 2

# Suppress FutureWarning about element-wise comparison to None
# Occurs when calling PSA plotting functions
import warnings

1) Set up input data for PSA using MDAnalysis

In [2]:
from MDAnalysis import Universe
from MDAnalysis.analysis.psa import PSAnalysis
from pair_id import PairID

Initialize lists for the methods on which to perform PSA. PSA will be performed for four different simulations methods with three runs for each: DIMS, FRODA, rTMD-F, and rTMD-S. Also initialize a PSAIdentifier object to keep track of the data corresponding to comparisons between pairs of simulations.

In [3]:
method_names = ['DIMS', 'FRODA', 'GOdMD', 'MDdMD', 'rTMD-F', 'rTMD-S',
                'ANMP', 'iENM', 'MAP', 'MENM-SD', 'MENM-SP',
                'Morph', 'LinInt']
labels = [] # Heat map labels
simulations = [] # List of simulation topology/trajectory filename pairs
universes = [] # List of MDAnalysis Universes representing simulations

For each method, get the topology and each of three total trajectories (per method). Each simulation is represented as a (topology, trajectory) pair of file names, which is appended to a master list of simulations.

In [4]:
for method in method_names:
    # Note: DIMS uses the PSF topology format
    topname = 'top.psf' if 'DIMS' in method or 'TMD' in method else 'top.pdb'
    pathname = 'fitted_psa.dcd'
    method_dir = 'methods/{}'.format(method)
    if method is not 'LinInt':
        for run in xrange(1, 4): # 3 runs per method
            run_dir = '{}/{:03n}'.format(method_dir, run)
            topology = '{}/{}'.format(method_dir, topname)
            trajectory = '{}/{}'.format(run_dir, pathname)
            labels.append(method + '(' + str(run) + ')')
            simulations.append((topology, trajectory))
    else: # only one LinInt trajectory
        topology = '{}/{}'.format(method_dir, topname)
        trajectory = '{}/{}'.format(method_dir, pathname)
        simulations.append((topology, trajectory))

Generate a list of universes from the list of simulations.

In [5]:
for sim in simulations:

2) Compute and plot nearest neighbor distances using PSA

Initialize a PSA comparison from the universe list using a C$_\alpha$ trajectory representation, then generate PSA Paths from the universes.

In [6]:
psa_hpa = PSAnalysis(universes, path_select='name CA', labels=labels)

Compute full Hausdorff pairs analysis for all unique pairs of paths

Generate the Hausdorff nearest neighbors and Hausdorff pairs (frames and distances)

In [7]:
psa_hpa.run_pairs_analysis(neighbors=True, hausdorff_pairs=True)

Plot clustered heat maps using Ward hierarchical clustering. The first heat map is plotted with the corresponding dendrogram and is fully labeled by the method names; the second heat map is annotated by the Hausdorff distances.

Use PSA IDs to obtain data for the comparisons we want

Get the Simulation IDs and PSA ID for the second DIMS simulation (DIMS 2) and third rTMD-F simulation (rTMD-F 3).

Note: The comparison between a pair of simulations is assigned a unique PSA ID. Given the order in which simulations are added to PSA, the comparison between a pair of simulations can be identified by (distance) matrix indices. The PSA ID is the index in the corresponding distance vector of a given pair of simulations.

In [8]:
identifier = PairID()
for name in method_names:
    run_ids = [1] if 'LinInt' in name else [1,2,3]
    identifier.add_sim(name, run_ids)

In [9]:
s1, s2, s3 = 'DIMS 1', 'DIMS 2', 'rTMD-F 3'
pid1 = identifier.get_pair_id(s1, s2)
pid2 = identifier.get_pair_id(s2, s3)

Plotting nearest neighbors as a function of normalized progress frame for:

DIMS 1 and DIMS 2

In [10]:
psa_hpa.plot_nearest_neighbors(filename='nn_dims1_dims2.pdf', idx=pid1, labels=(s1, s2))

<matplotlib.figure.Figure at 0x7faeb8fc7b50>

DIMS 1 and rTMD-F 3

In [11]:
psa_hpa.plot_nearest_neighbors(filename='nn_dims2_tmds3.pdf', idx=pid2, labels=(s2, s3))

<matplotlib.figure.Figure at 0x7faebfac9ed0>