README update

README.md

@@ -68,6 +68,16 @@ some_dir/
 
 After generating sequences, we automatically calculate TM-scores to evaluate the simliarity of the generated sequences and the original sequences. You may want to append the `--skiptm` argument to the above command if you wish to skip the very time-consuming calculation of TM scores against training set; doing so takes about ~1min per generated example on a 128-core machine running fully parallelized. 
 
+### Visualizing "folding" process
+
+The above sampling code can also be run with the ``--fullhistory`` flag to write an additional subdirectory `sample_history` under each of the `sampled_angles` and `sampled_pdb` folders that contain pdb/csv files coresponding to each timestep in the sampling process. The pdb files, for example, can then be passed into the script under `protdiff/pymol_vis.py` to generate a gif of the folding process. An example command to do this is:
+
+```bash
+python ~/protdiff/protdiff/pymol_vis.py pdb2gif -i sampled_pdb/sample_history/generated_0/*.pdb -o generated_0.gif
+```
+
+**Note** this script lives separately from other plotting code because it depends on PyMOL; feel free to install/activate your own installation of PyMOL for this. 
+
 ## Generating residues for protein backbones
 
 One way to evaluate the quality of generated backbones is via their "designability". This refers to whether or not we can design an amino acid chain that will fold into the designed backbone. To evaluate this, we use the ESM inverse folding model to generate residues that are predicted to fold into our generated backbone, and use AlphaFold to check whether that generated sequence actually does fold into a structure comparable to our backbone.