A method for CDR-H3 optimization
H3-OPT requires installation of AbRSA, you can download it through the AbRSA website.
To install H3-OPT, we provide the conda environment of H3-OPT, run the following command: :
# conda create --name <env> --file requirements.txt
The CDR-H3 selection module and structure refinement methods are implemented by the Schrödinger Python API, you can assess Schrödinger modules following the instructions here.
We provide the template CDR-H3 database from SAbDab website,. These template structures are made available for use online. Please unzip this compressed file before using.
We provide a command line interface that effectively figure out the high confidence CDR-H3 loop by the CBM and graft the template loop onto models prediction by AlphaFold2 using the TGM.
usage: selection.py [-h] [--input_structure_dir INPUT_STRUCTURE_DIR]
[--output_structure_dir OUTPUT_STRUCTURE_DIR]
[--pdbname PDBNAME] [--tmp_dir TMP_DIR]
[--template_dir TEMPLATE_DIR] [--cutoff CUTOFF]
optional arguments:
-h, --help show this help message and exit
--input_structure_dir INPUT_STRUCTURE_DIR
Path to input PDB file, please use PDB format files as
inputs
--output_structure_dir OUTPUT_STRUCTURE_DIR
Path to output PDB directory
--pdbname PDBNAME Pdbname of input PDB file
--tmp_dir TMP_DIR Path to renumbering files
--template_dir TEMPLATE_DIR
Path to CDR-H3 template files
--cutoff CUTOFF specify the cutoff of high confidence
To predict the CDR-H3 loops of input AF2 models, you can run the following command to extract the residue-level features and pair representations of input models.
usage: data_prep.py [-h] [--input_structure_dir INPUT_STRUCTURE_DIR]
[--feature_dir FEATURE_DIR] [--tmp_dir TMP_DIR]
[--pdbname PDBNAME]
optional arguments:
-h, --help show this help message and exit
--input_structure_dir INPUT_STRUCTURE_DIR
Path to input PDB file, please use PDB format files as
inputs
--feature_dir FEATURE_DIR
Path to output feature directory
--tmp_dir TMP_DIR Path to renumbering files
--pdbname PDBNAME Pdbname of input PDB file
To directly predict the 3D coordinates of CDR-H3 loops, we provide the weight of H3-OPT online. You can specify the path to model weight file and obtain the csv file which contains the coordinates of all Cα atoms in H3 loop.
usage: predict.py [-h] [--feature_dir FEATURE_DIR] [--model_dir MODEL_DIR]
[--out_dir OUT_DIR] [--out_name OUT_NAME]
[--pdbname PDBNAME]
optional arguments:
-h, --help show this help message and exit
--feature_dir FEATURE_DIR
Path to feature files
--model_dir MODEL_DIR
Path to model directory
--out_dir OUT_DIR Path to output PDB directory
--out_name OUT_NAME filename of predicted cordinate files
--pdbname PDBNAME Pdbname of input PDB file
You can optimize the conformation of input AlphaFold2 model CDR-H3 loop by running the following command lines.
usage: structure_generation.py [-h]
[--input_structure_dir INPUT_STRUCTURE_DIR]
[--tmp_dir TMP_DIR]
[--output_structure_dir OUTPUT_STRUCTURE_DIR]
[--pdbname PDBNAME] [--pred_csv PRED_CSV]
optional arguments:
-h, --help show this help message and exit
--input_structure_dir INPUT_STRUCTURE_DIR
Path to input PDB file, please use PDB format files as
inputs
--tmp_dir TMP_DIR Path to renumbering files
--output_structure_dir OUTPUT_STRUCTURE_DIR
Path to output PDB directory
--pdbname PDBNAME Pdbname of input PDB file
--pred_csv PRED_CSV filename of predicted cordinate files
You can get a confidence score of our prediction using following command lines.
usage: pred_confidence_score.py [-h] [--feature_dir FEATURE_DIR] [--model_dir MODEL_DIR]
[--out_dir OUT_DIR] [--out_name OUT_NAME]
[--pdbname PDBNAME]
optional arguments:
-h, --help show this help message and exit
--feature_dir FEATURE_DIR
Path to feature files
--model_dir MODEL_DIR
Path to model directory
--out_dir OUT_DIR Path to output PDB directory
--out_name OUT_NAME filename of predicted cordinate files
--pdbname PDBNAME Pdbname of input PDB file