Thanks for developing this module for detecting the MSM states. I am new to PyEMMA. I want to apply this package to understand the metstable states present in my system and wish to do TP analysis. I have tried to follow the BPTI tutorial and tried to follow few papers in which they have used PyEMMA. My issue is similar to #928.
My dataset is 500 ns (47000 frames) for a 1286 atom system (after getting rid of solvent).
I am using a distance featurizer between the COM of two nucleotides ina DNA base-pair, and a tICA reduction to 2 dimensions was done.
The clustering was done using
clk= coor.cluster_regspace(tica_concatenated,dmin=0.1,n_jobs=32, fixed_seed=True). The image of tICA plot is attached below.
the its its = msm.timescales_msm(dtrajs, lags=100, nits=5, n_jobs=4, errors='bayes'), resulted in
print('fraction of states used = ', msm_ba.active_state_fraction)-- 0.32
print('fraction of counts used = ', msm_ba.active_count_fraction) -- 0.38.
Since, it is less than 1, could you please suggest what kind of analysis I should do next? I would be highly grateful, if you could provide a link to a source or a notebook dealing with similar issues would help.
Thanks for developing this module for detecting the MSM states. I am new to PyEMMA. I want to apply this package to understand the metstable states present in my system and wish to do TP analysis. I have tried to follow the BPTI tutorial and tried to follow few papers in which they have used PyEMMA. My issue is similar to #928. My dataset is 500 ns (47000 frames) for a 1286 atom system (after getting rid of solvent).
I am using a distance featurizer between the COM of two nucleotides ina DNA base-pair, and a tICA reduction to 2 dimensions was done. The clustering was done using clk= coor.cluster_regspace(tica_concatenated,dmin=0.1,n_jobs=32, fixed_seed=True). The image of tICA plot is attached below.
the its its = msm.timescales_msm(dtrajs, lags=100, nits=5, n_jobs=4, errors='bayes'), resulted in