Conformational analysis of MD trajectories based on (pivot-based) Stochastic Proximity Embedding using dihedral distance as a metric (https://github.com/jeeberhardt/unrolr).
The installation instructions, documentation and tutorials can be found on readthedocs.org.
from unrolr import Unrolr
from unrolr.feature_extraction import Dihedral
from unrolr.utils import save_dataset
top_file = 'examples/inputs/villin.psf'
trj_file = 'examples/inputs/villin.dcd'
# Extract all calpha dihedral angles from trajectory and store them into a HDF5 file
d = Dihedral(top_file, trj_file, selection='all', dihedral_type='calpha').run()
X = d.result
save_dataset('dihedral_angles.h5', "dihedral_angles", X)
# Fit X using Unrolr (pSPE + dihedral distance) and save the embedding into a csv file
# The initial embedding is obtained using PCA (init = 'pca') with the OpenCL implementation
# to run SPE, a CPU implementation can be used as an alternative (platform='CPU')
U = Unrolr(r_neighbor=0.27, n_iter=50000, init='pca', platform='OpenCL', verbose=1)
U.fit_transform(X)
U.save(fname='embedding.csv')
print('%4.2f %4.2f' % (U.stress, U.correlation))- Compare SPE performance with UMAP
- Compatibility with python 3
- Compatibility with the latest version of MDAnalysis (==0.17)
- Unit tests
- Accessible directly from pip
- Improve OpenCL performance (global/local memory)
Eberhardt, J., Stote, R. H., & Dejaegere, A. (2018). Unrolr: Structural analysis of protein conformations using stochastic proximity embedding. Journal of Computational Chemistry, 39(30), 2551-2557. https://doi.org/10.1002/jcc.25599
MIT