Self Organising Maps for the Analysis of Molecular Dynamics Data
Compute average property
calc.distances
Calculation of Distances
Concatenate simulations
Clustering of Pathways
Cluster Representatives
Compute transition matrix
Coordinate superposition
Map the property vector to colours
Convert transition matrix to an igraph object
Select native contact distances
Get Neuron Population
Neuron representative
print.struct
Print Trajectory
Read gro file
Read structure files
Read trj file
map data to existing SOM
Read xtc trajectory file
Read xtc trajectory file
Read xtc trajectory file
Read xtc trajectory file
Write xtc trajectory file
Silhouette profile
Silhouette score
Add circles to SOM
Add legend clusters
Add Neuron Numbering
Stride a trj
Convert structure to pdb object
Summarizing a structure object
Summarizing a trajectory object
Trace pathway
Extract frame to pdb
Convert Trajectory to xyz
Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) <doi:10.1021/acs.jctc.1c01163>. I/O functions for xtc format files were implemented using the 'xdrfile' library available under open source license. The relevant information can be found in inst/COPYRIGHT.