TTclust is a python program used to clusterize molecular dynamics simulation trajectories. It only requires a trajectory and a topology file (compatible with most molecular dynamic packages such as Amber, Gromacs, Chramm, Namd or trajectory in PDB format thanks to the MDtraj package). Easy to use, the program produces a visual feedback of the clustering through a dendrogram graph. Other graphic representations are made to describe all clusters (see OUTPUT EXAMPLE part for more details).

This program is compatible with python 2.7.x and 3.x. You can have a warning for matplotlib with python 3.x but the program still works

##GUI If you want to use a Graphical User Interface instead, you can install ttclustgui after ttclust (using pip).
Link to the GUI : https://github.com/tubiana/ttclustGUI

If you want an easy installation of ttclust, I suggest these steps:

1. If you don't have conda (or Python) installed, Install miniconda (https://docs.conda.io/en/latest/miniconda.html) (with Python3, it's the future...)
2. Install with the command conda install -c tubiana -c conda-forge ttclust

1. Install numpy and cython with pip : pip install cython numpy
2. Install ttclust pip install ttclust

I strongly suggest you to use conda because pip will compile mdtraj and increase the the chance that the install fails (cgg/microsoft visual C++ library....).

1. Clone this repo git clone https://github.com/tubiana/ttclust

2. Install dependancies:

   • Using pip (and use python environment system)
     sudo pip install -r requirements.txt

   • using conda, suggested (and use a virtual conda environment, leaving your python installation untouched)
     conda env create -f environment.yml

     • Don't forget to activate environement with conda activate ttclust

3. Use ttclust scripts with python {PATH}/ttclust/ttclust.py or python {PATH}/ttclust/ttclustGUI.py

Note : sometimes mdtraj is hard to install. If you use PIP, please install manually cython before in this case sudo pip install cython then sudo pip install -r requirements.txt.
If you have still issues in installing mdtraj, you can install it with conda with conda install mdtraj

If you have issues with pip, I suggest you install ANACONDA and restart your terminal afterwards. Then, you need to install wxPython with conda conda install wxPython. Finally, you can use the PIP commmand: sudo pip install -r requirements.txt

If you have issues with pip installing mdtraj (Microsoft Visual C++ Build Tools missing), I also suggest you install ANACONDA and restart yout terminal afterwards. Then, you can mdtraj with conda conda install mdtraj. Finally, you can use the PIP commmand: sudo pip install -r requirements.txt

NOTE if ttclust was installed with pip, ttclustGUI will not work due to the gooey package (I hope it will be fixed soon..)
But you can still use the GUI with the script by cloning this repo and execute ttclustGUI.py

If you have issues with pip, first try to add to pip the --ignore-installed argument : sudo pip install --ignore-installed -r requirements.txt If it still doesn't work, it's maybe because of the System Integrity Protection (SIP). I suggest you in this case install ANACONDA or MINICONDA and restart your terminal afterwards. Normally, the pip command should work because your default python will be the anaconda (or miniconda) python.

If you have still issues with the GUI or missing packages : install with pip :
pip install wxpython==4.0.0b1
pip install pandas
pip install ttclust

To activate autocompletion for the argpase module, you have to use this command (only once): sudo activate-global-python-argcomplete

Following packages are needed:

• argparse
• argcomplete (for autocompletion, optional)
• cython (for mdtraj)
• mdtraj (version >= 0.17)
• progressbar
• datetime (a python library standard)
• glob (a python library standard)
• matplotlib
• scipy (version >= 0.18)
• prettytable
• sklearn (version >= 0.18)
• RXPY>=0.1.0 (FOR GUI)
• wxpython>=4.0.0b1 (FOR GUI)
• Pillow>=4.3.0 (FOR GUI)
• psutil>=5.4.2 (FOR GUI)
• gooey (FOR GUI)

For Selection syntax, use the one from MDTraj (https://mdtraj.org/latest/atom_selection.html). You can specify different selections for the calculation:

• st is used to extract a part of the trajectory (if this one is too big). ..* leave this blank if you want to keep your trajectory intact.
• sa used to align your trajectory on the same reference (eg: a chain or ..* the backbone)
• sr is used for the clustering (rmsd calculated on the atom selected with ..* this string)

MDTRAJ doesn't have nucleic acid keywords yet. We've implemented some keywords that will be altered to match DNA/RNA.... Keywords added :

• dna : selection based on the residue name (DA/DT/DC/DG)
• rna : selection based on the residue name (A/T/G/C or RA/RT/RG/RC)
• backbone_na : backbone of nucleic acid. Selection based on the residue name and atom name (P, O3', O5', C3', C4', C5')
• base : selection base on the residue name and atom name. Select RNA or DNA and exclude backbone_na, sugar's atoms and hydrogen
• base_rna : same as base but for RNA
• base_dna : same as base but for DNA

Those selection keywords can be used with other MDTRAJ selection keywords, e.g.:

• "protein and not dna"
• "rna and not type H"

With the scipy module, several methods for clustering are available. Change the method used with the -m argument. Methods available are:

• single
• complete
• average
• weighted
• centroid
• median
• ward (DEFAULT)

4 possibilities are available for the calculation:

1. Autoclustering (default method). The autoclustering uses the elbow method to find the optimum cluster numbers.
2. Give the number of clusters you want. Eg: if you want 3 clusters, use the argument
   -ng 3
3. Give a cutoff for the clustering. The final clusters are made from a dendrogram and this cutoff is used for the distance cutoff. If you want to ..* set this cutoff manually, use the argument
   -cc x.x (x.x is the cutoff)
4. Choose your cutoff by clicking on the matplotlib windows (on the dendrogram) in this case don't use the other arguments. recommended for the first clustering

The distance matrix can be long to calculate depending on your trajectory size. That's why this matrix is saved on the ".npy" format, in order to be used later. The name of the matrix will be the name of your selection string for clustering (sr) If you use the same selection string for clustering (sr) the matrix will be detected and the programme will ask you if you want to use it again (Y), recalculate this matrix (N) or choose another matrix (O). If you want to use the saved matrix without this interactive question) add in argument -i n which will deactivate the interactive prompt.

  -h, --help            show this help message and exit
  -f TRAJ, --traj TRAJ  trajectory file
  -t TOP, --top TOP     topfile
  -o OUTPUT, --output OUTPUT (default: clustering.log)
                        logfile 
  -st SELECT_TRAJ, --select_traj SELECT_TRAJ (default: all)
                        selection syntax for trajectory extraction, with QUOTE 
  -sa SELECT_ALIGNEMENT, --select_alignement SELECT_ALIGNEMENT (default: backbone)
                        selection syntax for alignement with QUOTE
						If you don't want alignement: use "none"
  -sr SELECT_RMSD, --select_rmsd SELECT_RMSD (default: backbone)
                        selection syntax for RMSD with QUOTE 
  -m METHOD, --method METHOD (default: ward)
                        method for clustering: single; complete; average;
                        weighted; centroid; median and ward
  -cc CUTOFF, --cutoff CUTOFF
                        cutoff for clusterization from hierarchical clusturing
                        with Scipy. If you choose to click on the graph, cutoff
                        will be the clicked value 
  -ng NGROUP, --ngroup NGROUP
                        number of group wanted. Use the maxclust method to
                        clusterize in this case. If you specify "auto", kmeans clustering
						with the elbow algorithm is used to find the optimal number of
						clusters
  -aa AUTOCLUST, --autoclust AUTOCLUST
                        By default, autoclustering is activated. Autoclustering is desactivated
                        when specifiying anything other than "Y", a cutoff value ('-cc') or a 
                        number of group ('-ng') 
  -i INTERACTIVE, --interactive INTERACTIVE
                        Interactive mode for distance matrix (Y/n)

There is some example usage with the examples files given on the "example" folder. Please note that the trajectory is reduced to the backbone in order to reduce the size of the git archive. Caution: You have to put quote beside your selection string (for sr, st, and sa arguments)

• Simple usage (clustering on backbone, logfile is called clustering.log, output folder is "clustering") python ttclust.py -f examples/example.xtc -t examples/example.pdb
• Clustering on residues 30 to 200 and backbone python ttclust.py -f examples/example.xtc -t examples/example.pdb -sr "residue 30 to 200 and backbone" -l res30-200.log
• Clustering on CA atoms and save this part of the trajectory with a cutoff of 2.75 python ttclust.py -f examples/example.xtc -t examples/example.pdb -sr "name CA" -st "name CA" -cc 2.75 -l CA-c2.75.log
• Clustering on backbone of the protein and chain A (note that with mdtraj there is no chaine's name, but chaine ID starting from 0) with 10 clusters only python ttclust.py -f examples/example.xtc -t examples/example.pdb -sr "protein and backbone and chainid 0" -l backbone-chainA.log -ng 10
• Note For PDB trajectory, don't use the -t argument python TrajectoryClustering.py -f traj.pdb -st "protein" -sr "backbone"

You need to use pythonw instead of python.

PDBs are saved in a new folder Cluster_PDB.
You can find in the PDB name the cluster number, size and the representative frame of the cluster (ie the frame number of the saved structure)
Example: C1-f11-s44.pdb corresponds to the cluter 1 made of 44 structures and the saved frame (representative) is the frame 11.

In the log file you will find all arguments given to the program with cluster information:

• size: number of structures in the cluster
• representative frame: frame with lowest RMSD between all other frames of the cluster
• Members: all frames belonging to the cluster
• spread: mean RMSD between all frames in the cluster
• RMSD between clusters: A tab with the RMSD between clusters
• Average RMSD between clusters: the average RMSD between clusters.

A dendrogram is generated at the end of the clustering with the corresponding cluster colors. The name of this file will be the same as the logfile with a ".png" extension. example: example.log --> example.png
The grey horizontal line is the cutoff value used.

A linear projection of cluster is made for the trajectory. Every barline represents a frame and the color a cluster number. Note that:

• If less or equal than 12 clusters: a defined color map was made in this order: red, blue, lime, gold, darkorchid, orange, deepskyblue, brown, gray, black, darkgreen, navy
• Else, the matplotlib "hsv" color map is used but the color change according to the number of clusters.

A vertical barplot is generated to have an overview of the cluster size. Each bar color corresponds to the cluster's color in the LinearProjection's representation and dendrogram cluster's color.

A 2D projection of the distance(RMSD) between the representative frame of each cluster is made. The method used is the multidimentional scaling method from the sk-learn python module. We can follow the evolution of each cluster thanks to the relative distance between them. The color of the points is the same as for other graphs (i.e. cluster's color) and the radius of each point depends on the cluster's spread.

A plot of the distance matrix is also made and allows to easily visualize the distance between two frames.

This program is under the GNU GPLv3 licence, which means that anyone who distributes this code or a derivative work has to make the source available under the same terms, and also provides an express grant of patent rights from contributors to users.