gpcr-3D-annotation

GPCRapa is a random forest model capable of annotation of GPCR conformations as active or inactive.

Installation

You can create an environment from .yml file:

conda env create -f gpcr_3d_conf.yml
conda activate gpcr_3d_conf

All required packages and versions can be found in requirements.txt file.

GPCRapa was tested on Ubuntu 20.04.5 LTS, AMD® Ryzen 7 3800x 8-core processor × 16, NVIDIA Corporation TU117 [GeForce GTX 1650].

Usage

To look at usage example of GPCRapa see Tutorial.ipynb in /Notebooks/

Training

To train your own model on your own data use script model_train_script.py in /Notebooks/ . It is mandatory that names of the extracted framers should be in "(framename)_(number).pdb" format.

python3 model_train_script.py -mt svm -psm /gpcr-3D-annotation/Files/resources/GPCR_state_map.csv -pfl path_to_your_trajectories -dp /gpcr-3D-annotation/test/ -op /gpcr-3D-annotation/test/ -mop /gpcr-3D-annotation/test/

All options:

usage: model_train_script.py [-h] [-mt {randomforest,svm,xgboost}] [-psm PATH_TO_STATE_MAPPING] [-pfl PATH_TO_FRAMES_LOCATION] [-dp DIR_PATH] [-op OUT_PATH] [-mop MODEL_OUT_PATH]
                             [-psf PATH_TO_SAVE_FEATURES] [-nsplits N_SPLITS]

options:
  -h, --help            show this help message and exit
  -mt {randomforest,svm,xgboost}, --modeltype {randomforest,svm,xgboost}
                        model type that will be trained
  -psm PATH_TO_STATE_MAPPING, --path_to_state_mapping PATH_TO_STATE_MAPPING
                        path to state mapping file for your pdbs
  -pfl PATH_TO_FRAMES_LOCATION, --path_to_frames_location PATH_TO_FRAMES_LOCATION
                        path to the folder with frames folders
  -dp DIR_PATH, --dir_path DIR_PATH
                        path to save sequence of pdb file
  -op OUT_PATH, --out_path OUT_PATH
                        path to save generated mapping of pdb file
  -mop MODEL_OUT_PATH, --model_out_path MODEL_OUT_PATH
                        path to save model
  -psf PATH_TO_SAVE_FEATURES, --path_to_save_features PATH_TO_SAVE_FEATURES
                        path to save features
  -nsplits N_SPLITS, --n_splits N_SPLITS
                        number of splits for KFold CV

State file example is located in Files/resources/GPCR_state_map.csv

To use custom splitting function to split your data, use model_train_script_custom_split.py:

python3 model_train_script_custom_split.py -mt svm -psm /home/ilya/work/Projects/gpcr-3D-annotation/Files/resources/GPCR_state_map.csv -pfl /home/ilya/work/Finished_md/md_finished_23.02.2020/md_frames_for_train/ -dp /home/ilya/work/Projects/gpcr-3D-annotation/test/ -op /home/ilya/work/Projects/gpcr-3D-annotation/test/ -mop /home/ilya/work/Projects/gpcr-3D-annotation/test/ -psf /home/ilya/work/Projects/gpcr-3D-annotation/test/

All options:

usage: model_train_script_custom_split.py [-h] [-mt {randomforest,svm,xgboost}] [-psm PATH_TO_STATE_MAPPING] [-pfl PATH_TO_FRAMES_LOCATION] [-dp DIR_PATH] [-op OUT_PATH] [-mop MODEL_OUT_PATH]
                                          [-psf PATH_TO_SAVE_FEATURES]

options:
  -h, --help            show this help message and exit
  -mt {randomforest,svm,xgboost}, --modeltype {randomforest,svm,xgboost}
                        model type that will be trained
  -psm PATH_TO_STATE_MAPPING, --path_to_state_mapping PATH_TO_STATE_MAPPING
                        path to state mapping file for your pdbs
  -pfl PATH_TO_FRAMES_LOCATION, --path_to_frames_location PATH_TO_FRAMES_LOCATION
                        path to the folder with frames folders
  -dp DIR_PATH, --dir_path DIR_PATH
                        path to save sequence of pdb file
  -op OUT_PATH, --out_path OUT_PATH
                        path to save generated mapping of pdb file
  -mop MODEL_OUT_PATH, --model_out_path MODEL_OUT_PATH
                        path to save model
  -psf PATH_TO_SAVE_FEATURES, --path_to_save_features PATH_TO_SAVE_FEATURES
                        path to save features

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.