markovmodel / PyEMMA

πŸš‚ Python API for Emma's Markov Model Algorithms πŸš‚
http://pyemma.org
GNU Lesser General Public License v3.0
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similar looking TICs #1421

Closed and-tos closed 5 years ago

and-tos commented 5 years ago

Hi all,

I am struggling to understand the results of a TICA I am running on a protein-ligand trajectory. When plotting the first TICs against frame number, they all look quite similar, with transitions occurring at the same time. This seems quite strange to me. My understanding is that this would mean that each process described by the TICs has a similar implied time scale.

See attached plot for details.

I would really appreciate any guidance here.

Thanks!

PyEMMA 2.5.5

top='../build_amber_0/dry3300.pdb'
simlist=glob.glob('../*/sim*/*.nc')
feat=pyemma.coordinates.featurizer(top)

ligand=216
ind_arr = np.zeros((165,2))
for i in range(165):
    ind_arr[i][0] = ligand-1
    ind_arr[i][1] = i

feat.add_residue_mindist(residue_pairs=ind_arr, scheme='closest-heavy', threshold=0.5)
#check if featurization is right: feat.describe()

trajs=pyemma.coordinates.source(simlist, features=feat)

#TICA
for lag in [1,2,3,4,5]:
    dim=50
    tica = pyemma.coordinates.tica(trajs, lag=lag, dim=dim)
    tica_output = tica.get_output()
    fig, axes = plt.subplots(1, 2, figsize=(10, dim))
    for ic in range(dim):
        plt.subplot2grid((dim,1), (ic,0))
        plt.plot(tica_output[0][:,ic])
        plt.ylabel=('IC {0}'.format(ic))

    plt.savefig('tica_timecourse{0}.png'.format(lag), dpi=600)

tica_timecourse1

and-tos commented 5 years ago

This can be closed, I traced the issue back to a problem with the way I processed my trajectories. Apologies