sAMPpred-GAT

The implementation of the paper sAMPpred-GAT: Prediction of Antimicrobial Peptide by Graph Attention Network and Predicted Peptide Structure

Requirements

The majoy dependencies used in this project are as following:

python  3.7
numpy 1.21.6
tqdm  4.64.1
pyyaml  6.0
scikit-learn  1.0.2
torch  1.11.0+cu113
torch-cluster  1.6.0
torch-scatter  2.0.9
torch-sparse  0.6.15
torch-geometric  1.7.2
tensorflow  1.14.0
tensorboardX  2.5.1

More detailed python libraries used in this project are referred to requirements.txt. Check your CPU device and install the pytorch and pyG (torch-cluster, torch-scatter, torch-sparse, torch-geometric) according to your CUDA version.

Note that torch-geometric 1.7.2 and tensorflow 1.14.0 are required, becuase our trained model does not support the torch-geometric with higher version , and the model from trRosetta does not support the tensorflow with higher version.

The The installed pyG (torch-cluster, torch-scatter, torch-sparse, torch-geometric) must be a GPU version according to your CUDA. If you installed a wrong vesion, there will be some unexpected errors like rusty1s/pytorch_scatter#248 and pyg-team/pytorch_geometric#2040. We provide the installation process of pytorch and pyG in our environment for reference:

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113

pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric==1.7.2 -f https://data.pyg.org/whl/torch-1.11.0+cu113.html

Tools

Two multiple sequence alignment tools and three databases are required:

psi-blast 2.12.0
hhblits 3.3.0

Databases:

nrdb90(http://bliulab.net/sAMPpred-GAT/static/download/nrdb90.tar.gz)
NR(https://ftp.ncbi.nlm.nih.gov/blast/db/)
uniclust30_2018_08(https://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/uniclust30_2018_08_hhsuite.tar.gz)

nrdb90: We have supplied the nrdb90 databases on our webserver. You need to put it into the utils/psiblast/ directoy and decompress it.

NR:You can download NR dababase from https://ftp.ncbi.nlm.nih.gov/blast/db/. Note that only the files with format nr.* are needed. You need to download them can put them into the utils/psiblast/nr/ directory. The utils/psiblast/nr/ folder should contain nr.00.psq, nr.00.ppi, ..., nr.54.phd, etc..

uniclust30_2018_08:You can download it dababase from https://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/uniclust30_2018_08_hhsuite.tar.gz. Just decompress it in the directory utils/hhblits/ and rename this database folder to uniclust30_2018_08.

trRosetta: The structures are predicted by trRosetta(https://github.com/gjoni/trRosetta), you need to download and put the trRosetta pretrain model(https://files.ipd.uw.edu/pub/trRosetta/model2019_07.tar.bz2) and decompress it into utils/trRosetta/.

Note that all the defalut paths of the tools and databases are shown in config.yaml. You can change the paths of the tools and databases by configuring config.yaml as you need.

psi-blast and hhblist are recommended to be configured as the system envirenment path. Your can follow these steps to install them:

How to install psiblast

Download

wget ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.12.0/ncbi-blast-2.12.0+-x64-linux.tar.gz
tar zxvf ncbi-blast-2.12.0+-x64-linux.tar.gz

Add the path to system envirenment in ~/.bashrc.

export BLAST_HOME={your_path}/ncbi-blast-2.12.0+
export PATH=$PATH:$BLAST_HOME/bin

Finally, reload the system envirenment and check the psiblast command:

source ~/.bashrc
psiblast -h

How to install hhblits

You can download and install the hhblits througth conda quickly.

conda install -c conda-forge -c bioconda hhsuite==3.3.0

Check the installation:

hhblits -h

Feature extraction

generate_features.py is the entry of feature extraction process. An usage example is shown in generate_features_example.sh.

Run the example by:

chmod +x generate_features_example.sh
./generate_features_example.sh

The features of the examples will be genrerated if your tools and databases are configured correctly. Some common errors:

BLAST Database error means the nrdb90 or NR is failed to found.
ERROR: could not open file ... uniclust30_2018_08_cs219.ffdata means the uniclust30_2018_08 is failed to found.

If you want generate the features using your own file in fasta format, just follow the generate_features_example.sh and change the pathes into yours.

Usage

It takes 3 steps to train/test our model: (1) copy the train/test soucre files in fasta format, which is supplied in datasets folder, into the data folder. (2) generate features, including the predicted sturctures and the sequential features. (3) train / test.

train.py and test.py are used for training and testing, respectively. Running python train.py -h and python test.py -h to learn the meaning of each parameter.

The input folder should like:


-positive/
XXX(name of the positive file).fasta
--pssm/
---output/
----A.pssm
----B.pssm
---- ...
--hhm/
---output/
----A.hhm
----B.hhm
---- ...
--npz/
---A.npz
---B.npz

-negative
XXX(name of the negative file).fasta
 --pssm/
---output/
----C.pssm
----D.pssm
---- ...
--hhm/
---output/
----C.hhm
----D.hhm
---- ...
--npz/
---C.npz
---D.npz

The script generate_features_example.sh just generated the right folder structure. Just follow the example to generate the input folder.

Note that before you train and test the model, you must successfully run generate_features_example.sh.

Test

A trained model for XUAMP is supplied in saved_models/samp.model as an example. Run test.py to predict the example sequences:

python test.py

If you want test the specific dataset, for example XUAMP, you should copy the corresponding files in fasta format in datasets/independent test datasets/ directory into the data/test_data/positive/ and data/test_data/negative/, and set the args relative to the inputs. An example is given by test.sh:

chmod +x test.sh
./test.sh

Train

If you want train a model based on the specific dataset, for example XUAMP, you should copy the files in fasta format in datasets/train datasets/ directory into the data/train_data/positive/ and data/train_data/negative/, and set the args relative to the inputs. An example is given by train.sh:

chmod +x train.sh
./train.sh

When the training process finished, the saved_models/auc_XU_final.model(We have supplied a well trained model and rename it to samp.model) will be the model optimized by AUC as introduced in this paper .

HongWuL/sAMPpred-GAT