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TaRget Identification of AntiMicrobial Peptides with Heterogenious graph attention networks
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bcgsc/triAMPh
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TaRget Identification of AntiMicrobial Peptides with Heterogenious graph attention networkstriAMPh is a heteregenous graph attention network based species-specific antimicrobial bioactivity predictor. It also gives the users to flexibly define their own peptide and pathogen features. In this study, we usedESM2 andNucleotideTransformerv2 embeddings for peptides and pathogens respectively as their feauture vectors. As a backbone,this implementation ofHAN paper was adapted.
data:Contains the master data file used for training, validation, and testing.data/stratified:Specifically contains the training, validation, test split as well as message passing portions of the master data for competitor method developers convenience.data/protein_embs:Contains the example peptide embeddings. For each peptide, a single .npy file that contains a 2D array (per token/amino acid) embeddings. We expect users to follow this format. Here, we were only able to provide a subset of our trained peptides. If you want to reproduce the results please refer toESM2'sextract.pyscript and generate per_token embeddings with their defualt model.data/genomic_embs:Contains the example pathogen embeddings. For each pathogen, a single .npy file that contains a 2D array (per token/5.1kmer in our training scheme) embeddings. We expect users to follow this format.data/weights_selected:Contains the weights from our hyperparameter tuning rounds we selected as our best performance providing one. It uses the default configuration of the model.
imgs:Contains the abstract images for the readme file.results:Contains how an example result folder will be generated after the training process.results/weights:Contains the weights generated by each training process.results/accs:Contains the accuracy plots generated by each training run.results/losses:Contains the loss plots generated by each training run.
src:Contains the code for triAMPh:src/constants.py:Contains the constants used for triAMPh in a single file.src/dataset.py:Contains the data wrapper classes for training and testing scripts.src/model.py:Contains the deep learning models for triAMPh.src/train_triAMPh.py:The training, validation, and optionally testing script for triAMPh.src/test_triAMPh.py:The testing script for triAMPh.
triAMPh_env.yml:Contains the dependencies needed to run triAMPh.
- Please clone this repository by running:
git clone https://github.com/bcgsc/triAMPh.git- In order to run triAMPh, you need to download the dependencies specified in
triAMPh_env.yml. Please run the following command to create the environment.
conda env create -f triAMPh_env.yml.yml- Activate the environment by running:
conda activate triAMPh- You are all set!
Here, we expect users to specify one positive edge file and one negative edge file. triAMPh, based on user specified partitioning portions, splits the dataset into training, validation, and if they do not add up to 100%, testing sets.
usage: train_triAMPh.py [-h] -p POSITIVE_EDGES -n NEGATIVE_EDGES -e PROTEIN_EMB_DIR -g GENOMIC_EMB_DIR -o OUTPUT_DIR [--prefix PREFIX] [--tr_split TR_SPLIT] [--val_split VAL_SPLIT] [--msg_pas MSG_PAS] [--inductive INDUCTIVE] [--lr LR] [--epochs EPOCHS] --gen_emb_size GEN_EMB_SIZE --prot_emb_size PROT_EMB_SIZE [--han_input_size HAN_INPUT_SIZE] [--han_hidden_size HAN_HIDDEN_SIZE] [--n_heads N_HEADS] [--dropout DROPOUT] [--seed SEED]arguments: -h, --help show this help message and exit -p POSITIVE_EDGES, --positive_edges POSITIVE_EDGES Path to the file that contains the positive edges. Expects a .csv file. -n NEGATIVE_EDGES, --negative_edges NEGATIVE_EDGES Path to the file that contains the negative edges. Expects a .csv file. -e PROTEIN_EMB_DIR, --protein_emb_dir PROTEIN_EMB_DIR Path to the folder that contains the individual embeddings of peptides. Note: Files should be saved in .npy format. -g GENOMIC_EMB_DIR, --genomic_emb_dir GENOMIC_EMB_DIR Path to the folder that contains the individual embeddings of pathogens. Note: Files should be saved in .npy format. -o OUTPUT_DIR, --output_dir OUTPUT_DIR Path to the directory where the results will be saved atoptional arguments: --prefix PREFIX Prefix to be added to the filenames of the plots and weights generated. --tr_split TR_SPLIT Percentage of the training split from the provided data. --val_split VAL_SPLIT Percentage of the validation split from the provided data. --msg_pas MSG_PAS Percentage of the edges to be used for message passing. --inductive INDUCTIVE Training strategy: Inductive if 1, transductive otherwise. --lr LR Learning rate for training. --epochs EPOCHS Number of epochs to train for. --gen_emb_size GEN_EMB_SIZE Length of the genomic embedding vector. --prot_emb_size PROT_EMB_SIZE Length of the protein embedding vector. --han_input_size HAN_INPUT_SIZE Input length of the projected node vectors given to the Heterogeneous Graph Attention Network. --han_hidden_size HAN_HIDDEN_SIZE Length of the hidden/output node vectors of the Heterogeneous Graph Attention Network. --n_heads N_HEADS Number of attention heads for Heterogeneous Graph Attention Network. --dropout DROPOUT Dropout percent for Heterogeneous Graph Attention Network. --seed SEED Random seed to be set.Example usage:
python src/train_triAMPh.py\ -p data/positive_edges_triAMPh.csv -n data/negative_edges_triAMPh.csv\ -e data/protein_embs -g data/genomics_embs -o results\ --gen_emb_size 512 --prot_emb_size 1280 --epochs 100 --prefix exampleHere, we expect users to specify one positive edge file and one negative edge file each for message passing and testing/supervision.
usage: test_triAMPh.py [-h] -p POSITIVE_EDGES -n NEGATIVE_EDGES -t TEST_POSITIVE_EDGES -a TEST_NEGATIVE_EDGES -e PROTEIN_EMB_DIR -g GENOMIC_EMB_DIR -o OUTPUT_DIR -w WEIGHT_PATH [--threshold THRESHOLD] --gen_emb_size GEN_EMB_SIZE --prot_emb_size PROT_EMB_SIZE [--han_input_size HAN_INPUT_SIZE] [--han_hidden_size HAN_HIDDEN_SIZE] [--n_heads N_HEADS] [--seed SEED]arguments: -h, --help show this help message and exit -p POSITIVE_EDGES, --positive_edges POSITIVE_EDGES Path to the file that contains the message passing positive edges. Expects a .csv file. -n NEGATIVE_EDGES, --negative_edges NEGATIVE_EDGES Path to the file that contains the message passing negative edges. Expects a .csv file. -t TEST_POSITIVE_EDGES, --test_positive_edges TEST_POSITIVE_EDGES Path to the file that contains the message passing positive edges. Expects a .csv file. -a TEST_NEGATIVE_EDGES, --test_negative_edges TEST_NEGATIVE_EDGES Path to the file that contains the message passing negative edges. Expects a .csv file. -e PROTEIN_EMB_DIR, --protein_emb_dir PROTEIN_EMB_DIR Path to the folder that contains the individual embeddings of peptides. Note: Files should be saved in .npy format. -g GENOMIC_EMB_DIR, --genomic_emb_dir GENOMIC_EMB_DIR Path to the folder that contains the individual embeddings of pathogens. Note: Files should be saved in .npy format. -o OUTPUT_DIR, --output_dir OUTPUT_DIR Path to the directory where the results will be saved at -w WEIGHT_PATH, --weight_path WEIGHT_PATH Path to the pretrained weights of triAMPh.optional arguments: --threshold THRESHOLD Threshold value for binary cross entropy. Default: Above 0.5 positive, below negative. --gen_emb_size GEN_EMB_SIZE Length of the genomic embedding vector. --prot_emb_size PROT_EMB_SIZE Length of the protein embedding vector. --han_input_size HAN_INPUT_SIZE Input length of the projected node vectors given to the Heterogeneous Graph Attention Network. --han_hidden_size HAN_HIDDEN_SIZE Length of the hidden/output node vectors of the Heterogeneous Graph Attention Network. --n_heads N_HEADS Number of attention heads for Heterogeneous Graph Attention Network. --seed SEED Random seed to be set.Example usage:
python src/test_triAMPh.py\ -p data/split/msg_positive_edges.csv -n data/split/msg_negative_edges.csv\ --test_positive_edges data/split/test_positive_edges.csv --test_negative_edges data/split/test_negative_edges.csv\ -e data/protein_embs -g data/genomic_embs\ -o results --weight_path results/weights/weight_2025-03-19_11-00-08.pth\ --gen_emb_size 512 --prot_emb_size 1280triAMPh expects the inputs in a specific format. In this section, the formatting will be discussed.
We expect edge files to contain peptide IDs under the columnID, peptide sequences under the columnSequences, and pathogen names under the columnPathogens. The format of a file is expected to be a.csv.
triAMPh expects embeddings to be 2D arrrays saved in a separate.npy file for each peptide/pathogen. Here, the important thing is to make the file names match with IDs/pathogen names specified in the edge file.
Please use Github issues for problems related to the code and contact bucar at bcgsc.ca for further inquiries.