SPROF: To Improve Protein Sequence Profile Prediction through Image Captioning on Pairwise Residue Distance Map
These instructions will get you a copy of the project up and running on your local machine.
SPROF has been implemented in Python3.
Installing requirements:
pip3 install -r requirements.txt
or avoiding problems in multiple Python environments:
python3 -m pip install -r requirements.txt
if you want to generate features by 'raw_feature_generate.py', install dssp by
apt-get install dssp
This repository has include the CASP13 test set data on 'input/casp_test/', 'target/casp_test/' and raw features on 'raw_features/', raw pdbs on 'raw_pdbs/'.
if you want to generate other data for training/test:
Firstly, install dssp by 'apt-get install dssp' and deposite your pdbs(the examples of pdb in correct format are on 'raw_pdbs/') on 'raw_pdbs/'.
Secondly you should make a list file namd 'xxx.txt' (for example) which contains all your training/test pdbs' name.
Thirdly use the 'raw_feature_generate.py' to generate raw features:
usage: raw_feature_generate.py [-h] [--preprocess_list PREPROCESS_LIST]
--preprocess_list PREPROCESS_LIST
The path of your preprocess list.
The generated raw-features will be deposit on 'raw_features', use 'preprocess.py' to generate features for model training/test:
usage: preprocess.py [-h] [--preprocess_list PREPROCESS_LIST]
[--pdb_path PDB_PATH] [--features_path FEATURES_PATH]
[--input_path INPUT_PATH] [--target_path TARGET_PATH]
optional arguments:
-h, --help show this help message and exit
--preprocess_list PREPROCESS_LIST
he path of a preprocess pdb list.
--pdb_path PDB_PATH
the path of pdb
--features_path FEATURES_PATH
the path of features
--input_path INPUT_PATH
the path of input to save
--target_path TARGET_PATH
the path of target to save
The generated input&target data for training/test will be deposit on the input_path and target_path.
usage: train.py [-h] [--batch_size BATCH_SIZE] [--learning_rate LEARNING_RATE]
[--maximum_epoch MAXIMUM_EPOCH]
[--sequential_features SEQUENTIAL_FEATURES]
[--pairwise_features PAIRWISE_FEATURES]
[--target_output TARGET_OUTPUT]
[--train_list TRAIN_LIST] [--models_name MODELS_NAME]
optional arguments:
-h, --help show this help message and exit
--learning_rate LEARNING_RATE
The learning rate of ADAM optimization.
--maximum_epoch MAXIMUM_EPOCH
The maximum epoch of training
--sequential_features SEQUENTIAL_FEATURES
The full path of sequential features of training data.
--pairwise_features PAIRWISE_FEATURES
The full path of pairwise features of training data.
--target_output TARGET_OUTPUT
The full path of the target outputs.
--train_list TRAIN_LIST
The full path of the train list.
--models_name MODELS_NAME
The name of models.
The input data of training is too large to upload. To get these data, please refer to 'Data Preprocess' and use the 'train_list' as preprocess_list, if any problem, contact [email protected].
Example of training:
- Using default paramters to train:
python3 train.py
- Using different paramters to train:
python3 train.py --learning_rate=0.0005 --maximum_epoch=40 --sequential_features='input/casp_test/sequential_features' --pairwise_features='input/casp_test/pairwise_features' --target_output='target/casp_test' --train_list='train_list' --models_name='models'
Then you can obtain several models and save them in the your models_name directory.
usage: test.py [-h] [--sequential_features SEQUENTIAL_FEATURES]
[--pairwise_features PAIRWISE_FEATURES]
[--target_output TARGET_OUTPUT] [--feature_list FEATURE_LIST]
[--models_path MODELS_PATH]
optional arguments:
-h, --help show this help message and exit
--sequential_features SEQUENTIAL_FEATURES
The full path of sequential features of test data.
--pairwise_features PAIRWISE_FEATURES
The full path of pairwise features of test data.
--target_output TARGET_OUTPUT
The full path of the target outputs.
--feature_list FEATURE_LIST
The full path of the test list.
--models_path MODELS_path
parent path of your models.
The CASP13 test set has been included in the repository, so you can test our model's perpormance easily.
- Using default paramters to test:
python3 test.py
- Using different paramters test
python3 test.py --sequential_features='input/casp_test/sequential_features' --pairwise_features='input/casp_test/pairwise_features' --target_output='target/casp_test' --train_list='train_list' --models_path='models'
If you find this work useful in your research, please consider citing the paper:
"To Improve Protein Sequence Profile Prediction through Image Captioning on Pairwise Residue Distance Map"
https://pubs.acs.org/doi/10.1021/acs.jcim.9b00438
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent