Material for manifold learning techniques comparison on benchmark dataset

This archive contains the restricted 10 ensemble benchmark and the scripts used in the manifold learning techniques assessment. Files related to an ensemble are prefixed with ID1_ID2_, where ID1 is the first member in alphabetical order, and ID2 is the reference for the structural alignment.

The archive includes the following for each member of the benchmark:
A _mm.pdb file containing the ensemble's conformations.
A _aln.fa file, which is the multiple sequence alignment of the ensemble.
A _rmsd.txt file with the all pairwise root mean squared deviation (RMSD) of the ensemble.
A _raw_coords_ca.bin file with the raw coordinates in binary format.
A _raw_coords_ca_mask.bin file with the binary format gap coordinates.
A _features_pca.csv file detailing the positions of each sample in the ensemble's principal component space.
A _dist_to_hull.csv file with the ID of each ensemble member, their label in the clustering in the PC space, and the squared distance of this sample to the convex hull formed by members of the other clusters.
A _pca_errors.csv file containing the same information as the _dist_to_hull.csv file, but with the addition of the PCA reconstruction error, measured as the RMSD between the predicted and ground truth structures. The prediction of a sample is done by fitting the PCA to all clusters except the one being evaluated.
Three _XXX_kcpa_errors.json files with the kPCA reconstruction errors for each ensemble member, measured as the RMSD between the predicted and ground truth structures, using kPCA at different sigma and alpha parameters from the grid search. The XXX indicates the kernel used. The prediction of a sample is done by fitting the kPCA to all clusters except the one being evaluated.
A _umap_errors.json file with the UMAP reconstruction errors for each ensemble member, measured as the RMSD between the predicted and ground truth structures, using UMAP at different n_neigh and min_dist parameters from the grid search. The prediction of a sample is done by fitting the UMAP to all clusters except the one being evaluated. UMAP could be run only on a subset of the ensembles.
A _rbf_kpca_default_sigma.json file containing the kPCA reconstruction errors for each ensemble member, measured as the RMSD between the predicted and ground truth structures, using kPCA with RBF kernel at the default alpha and sigma parameters. The prediction of a sample is done by fitting the kPCA to all clusters except the one being evaluated.
A _rbf_kpca_errors_real.json file with the kPCA reconstruction errors for each ensemble member, measured as the RMSD between the predicted and ground truth structures, using kPCA with RBF kernel with a predicted optimal sigma parameter and alpha parameters of 1.0, 1e-5, and 1e-6. The prediction of a sample is done by fitting the kPCA to all clusters except the one being evaluated.
The scripts used to generate the convex hull and for the PCA-kPCA comparison are as follows:
dist_to_hull.py computes the coordinates in the PC space of each member, divides the members into clusters, and computes the distance of each member to the convex hull formed by members of the other clusters in the PC space. This script uses polytope_app.cpp with a Python binding to compute the squared distance of each member to the convex hull. polytope_module.so is the compiled C++ module called by the Python script.
interpol_apase.py computes the interpolation in the ATPase latent space, and outputs the .pdb files of the trajectories.
pca_kpca.py calculates the reconstruction error for both PCA, kPCA, and UMAP for each ensemble member by fitting the PCA, kPCA, or UMAP to all members of other clusters, excluding the cluster of the member currently being evaluated.
A procheck folder containing summary tables of the procheck analysis on original and reconstructed structures.
The stats.csv file contains descriptive information about the benchmark. Please consult the related documentation to understand the meaning of each column in this file.