Frequently Asked Questions

1. How do I decrease / increase the number of threads used during clustering, assignment, and rmsd calculation?

Set the

OMP_NUM_THREADS

environment variable to the desired number of threads. In linux, you would type (or add to your bashrc):

export OMP_NUM_THREADS=6

  1. I see the following error. What do I do?

    #004: H5Z.c line 1095 in H5Z_pipeline(): required filter is not registered

    You are trying to read an HDF5 file that was written using a different PyTables installation. Your current PyTables installation is likely missing the compression algorithm (filter) required to read the file. The solution is to find a version of Pytables that has the old compression algorithm (filter) and use MSMBuilder to read and then re-write the trajectories (by default, MSMBuilder uses the PyTables BLOSC compression). To do this (for a single File), you would so something like:

    from msmbuilder import Trajectory
R1 = Trajectory.LoadFromLHDF(Filename)
R1.SaveToLHDF(NewFilename)

  2. I received an “Illegal instruction” error. What does this mean?

    MSMBuilder2 requires an SSE3 compatible processor when Clustering and calculating RMSDs. Any processor built after 2006 should have the necessary instructions.

  3. In my implied timescales plot, I see unphysically slow timescales.

    The current estimators for transition matrices are somewhat sensitive to poor statistics. The hybrid k-centers / k-kmedoids clustering focuses on providing the best possible clustering–without regard to the quality of the resulting statistics. Thus, to get more precise timescales, you may have to find a way to achieve better statistics. Here are a few ideas:

    1. Collect longer trajectories.
    2. Use fewer states. Also, by increasing the number of local and global k-medoid updates, you can often increase the accuracy of your clustering while simultaneously lowering the number of states.
    3. Subsample your data when clustering.
    4. Skip the initial k-centers step of clustering, instead using randomly selected conformations. This generally leads to poorer clustering quality, but considerably better statistics in each state. (Thus, the clusters will be much more localized to regions of high population density.) This can be achieved by setting “-r 0” when clustering.
    5. Use Ward clustering

  4. Why are there -1s in my Assignments matrix?

    We use -1 as a “padding” element in Assignment matrices. Suppose your project has maximum trajectory length of 100. If trajectory 0 has length 50, then A[0,50:] should be a vector of -1. Furthermore, when you perform trimming to ensure (strong) ergodicity, futher -1s could be introduced at the start or finish of the trajectory. Finally, if Ergodic trimming was performed with count matrices estimated using a sliding window, you could even see something like: -1 -1 -1 x -1 -1 y z … This is because sliding window essentially splits your trajectory into independent subtrajectories–one for each possible window starting position. “x” then marks the start of one of these subtrajectories.

  5. When building MSMBuilder, I see an LPRMSD error. What should I do?

    ***************************************************************************
WARNING: The C extension 'LPRMSD' could not be compiled.
This may be due to a failure to find the BLAS libraries
***************************************************************************

    Don’t worry. This module is not used by any of the standard MSMBuilder features.

  6. What is the difference between PCCA+ and FPCCA+?

    FPCCA+ is PCCA+ with a different choice of eigenvectors to model. In particular, FPCCA+ uses a criterion based on both timescale and eigenvector flux.

  7. Should I use FPCCA+ or PCCA+?

    First, note that FPCCA+ is more “lossy” or “coarse-grained” than PCCA+. By discarding slow but high-flux eigenvectors, you are losing some information from your microstate model. Essentially, the choice between FPCCA+ and PCCA+ depends on how much you weight model accuracy versus model simplicity.

Q9. I see warnings when using PCCA+:

ComplexWarning: Casting complex values to real discards the imaginary part
RuntimeWarning: invalid value encountered in cdouble_scalars
Warning: constraint violation detected.
f = nan

This is probably due to PCCA+ finding a “degenerate” state decomposition, where one of your macrostates is empty. Usually, the minimization procedure should eventually find a feasible point with the correct number of states. Be sure to check that your resulting state decomposition makes sense.

  1. How do I make an MSM movie?

    To build a movie, you just Sample states from the model (MSMLib.Sample). Then you sample conformations from each state (Project . GetRandomConfsFromState). Then you append each frame to a PDB file (Conformation.SavePDB or Trajectory.SavePDB). After you have the PDBs, you can use either VMD or pymol for movie making.

11. On an OSX Lion Machine, clustering fails with an “Abort Trap” error message. What do I do?

This is due to a known bug in OSX Lion’s support for OpenMP (see https://discussions.apple.com/thread/3786045?start=0&tstart=0). As a workaround, you can simply

export OMP_NUM_THREADS=1

to disable OpenMP support during clustering. This should eliminate the problem, but it limits you to single core clustering.

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