AMPlify is an attentive deep learning model for antimicrobial peptide prediction.

For more information, please refer to the preprint: https://www.biorxiv.org/content/10.1101/2020.06.16.155705v1

• Python 3.6
• Keras 2.2.4
• Tensorflow 1.12
• Numpy <1.17
• Pandas
• Scikit-learn
• Biopython
• h5py <3

1. Create a new conda environment:

conda create -n amplify python=3.6

2. Activate the environment:

conda activate amplify

3. Install AMPlify in the environment:

conda install -c bioconda amplify

train_amplify and AMPlify can now be run. See usage information below.

4. To deactivate an active environment, use:

conda deactivate

Datasets for training and testing are stored in the data folder. Please specify the directory if you would like to use those datasets for training or testing the model.

Weights for 5 pre-trained sub-models are stored in the models folder.

Usage: train_amplify [-h] -amp_tr AMP_TR -non_amp_tr NON_AMP_TR [-amp_te AMP_TE] [-non_amp_te NON_AMP_TE] -out_dir OUT_DIR -model_name MODEL_NAME

optional arguments:
  -h, --help            Show this help message and exit
  -amp_tr AMP_TR        Training AMP set, fasta file
  -non_amp_tr NON_AMP_TR
                        Training non-AMP set, fasta file
  -amp_te AMP_TE        Test AMP set, fasta file, optional
  -non_amp_te NON_AMP_TE
                        Test non-AMP set, fasta file, optional
  -out_dir OUT_DIR      Output directory
  -model_name MODEL_NAME
                        File name of trained model weights

Example: train_amplify -amp_tr ../data/AMP_train_20190414.fa -non_amp_tr ../data/non_AMP_train_20190414.fa -amp_te ../data/AMP_test_20190414.fa -non_amp_te ../data/non_AMP_test_20190414.fa -out_dir ../models/ -model_name model

Expected output: 1) The model weights trained using the specified data; 2) Test set performance, if test sequences have been specified.

Usage: AMPlify [-h] [-md MODEL_DIR] [-m MODEL_NAME MODEL_NAME MODEL_NAME MODEL_NAME MODEL_NAME] -s SEQS [-od OUT_DIR] [-of {txt,tsv}] [-att {on,off}]

optional arguments:
  -h, --help            Show this help message and exit
  -md MODEL_DIR, --model_dir MODEL_DIR
                        Directory of where models are stored (optional)
  -m MODEL_NAME MODEL_NAME MODEL_NAME MODEL_NAME MODEL_NAME, --model_name MODEL_NAME MODEL_NAME MODEL_NAME MODEL_NAME MODEL_NAME
                        File names of 5 trained models (optional)
  -s SEQS, --seqs SEQS  Sequences for prediction, fasta file
  -od OUT_DIR, --out_dir OUT_DIR
                        Output directory (optional)
  -of {txt,tsv}, --out_format {txt,tsv}
                        Output format, txt or tsv (optional)
  -att {on,off}, --attention {on,off}
                        Whether to output attention scores, on or off (optional)

Example: AMPlify -s ../data/AMP_test_20190414.fa

Expected output: Predicted confident scores and classes of the input sequences. Results for invalid sequences will be filled with NA.

Additional scripts and data for our AMP discovery pipeline are provided in the auxiliary folder. Parameters for GMAP and MAKER2 are described in the Methods section of the manuscript.

Chenkai Li ([email protected])

If you have any questions, comments, or would like to report a bug, please file a Github issue or contact us.