DeepCGSA---Accurate estimation of solvent accessible surface area for coarse-grained biomolecular structures with deep learning
We provide the python code of DeepCGSA implementation for estimating SASA based on CG structures.
numpy==1.19.4
tensorflow==2.0.0
pandas==1.0.3
sklearn==0.23.1
biopandas==0.2.7
scipy==1.5.4
biopython==1.78 (with NACCESS)
Usage: DeepCGSA.py [options]
Options:
-h, --help show this help message and exit
-f PDB_FILENAME, --file=PDB_FILENAME
The PDB file containing a CG protein or RNA structure
as discussed in paper. Please use same format as shown
in example input files (pdb format): example_CA.pdb,
example_CACB.pdb ......
-t CG_TYPE, --CG_type=CG_TYPE
CG type of the PDB file, available for CA (Cα protein
structure), CACB (Cα-Cβ protein structure), martini
(Martini protein strcture), P (P-based RNA structure),
3SPN (3SPN RNA structure), AA (all-atom structure,
calculate by NACCESS).
-o OUTPUT_FILENAME, --output=OUTPUT_FILENAME
Residue-wise prediction will write to a csv file named
as output_filename.csv
-c CREATE_CG, --create_CG_file=CREATE_CG
We provided a convenient function to create CG file
with appropriate format from AA file. -c option gave
the CG type to convert. When using -c option, script
will not calculate SASA but output a CG file. For
example: "python DeepCGSA.py -f example_AA.pdb -c CA".
Created file will be named as xxx_CGtype.pdb
model_weight should be copied to current work path with DeepCGSA.py together.
We provide input files with appropriate format: example_CA.pdb, example_CACB.pdb ......
For example, run the following code to estimate SASA of the example Cα pdb file.
python DeepCGSA.py -f example_CA.pdb -t CA -o prediction
The example.ipynb provides a simple usage of DeepCGSA.