This repository contains scripts for training a generative long short-term memory recurrent neural network on peptide sequences. The user can provide sets of amino acid sequences to train the model, and finally invoke sampling of sequences that should be similar to the training data. As such, artificial intelligence is put in charge of de novo design of new peptide sequences. The code in this repository relies on the keras package by Chollet and others (https://github.com/fchollet/keras) with tensorflow backend (http://tensorflow.org) as well as on sklearn (http://scikit-learn.org) and modlamp (https://modlamp.org).

• README.md: this file
• LSTM_peptides.py: contains the main code in the following two classes:
  • SequenceHandler: class that is used for reading amino acid sequences and translating them into a one-hot vector encoding.
  • Model: class that generates and trains the model, can perform cross-validation and plot training and validation loss.
• requirements.txt: requirements / package dependencies
• LICENSE: MIT opensource license

Clone the directory to your computer by:

git clone https://github.com/alexarnimueller/LSTM_peptides

Then, install all requirements (in requirements.txt). In this folder, type:

pip install -r requirements.txt

Finally run the model as follows (with your own parameters provided, see the list below):

python LSTM_peptides.py --dataset $TRAINING_DATA_FILE --name $YOUR_RUN_NAME $FURTHER_OPTIONAL_PARAMETERS

• dataset (default=training_sequences_noC.csv)
  • file containing training data with one sequence per line
• name (default=test)
  • run name for all generated data; a new directory will be created with this name
• batch_size (OPTIONAL, default=128)
  • Batch size to use by the model.
• epochs (OPTIONAL, default=50)
  • Number of epochs to train the model.
• layers (OPTIONAL, default=2)
  • Number of LSTM layers in the model.
• neurons (OPTIONAL, default=256)
  • Number of units per LSTM layer.
• cell (OPTIONAL, default=LSTM)
  • type of neuron to use, available: LSTM, GRU
• dropout (OPTIONAL, default=0.1)
  • Fraction of dropout to apply to the network. This scales with depth, so layer1 gets 1*dropout, Layer2 2*dropout etc.
• mode (OPTIONAL, choices=[pretrain. finetune, sample], default=pretrain)
  • Whether to pre-train (pretrain), fine-tune (finetune) or just sample from a pre-trained model (sample).
• valsplit (OPTIONAL, default=0.2)
  • Fraction of the data to use for model validation. If 0, no validation is performed.
• sample (OPTIONAL, default=100)
  • Number of sequences to sample from the model after training.
• temp (OPTIONAL, default=1.25)
  • Temperature to use for sampling.
• maxlen (OPTIONAL, default=0)
  • Maximum sequence length allowed when sampling new sequences. If 0, the longest sequence length of the training data is maxlen
• startchar (OPTIONAL, default=B)
• lr (OPTIONAL, default=0.01)
  • Learning rate to be used for Adam optimizer.
• modfile (OPTIONAL, default=None)
  • If train=False, a pre-trained model file needs to be provided, e.g. modfile=./checkpoint/model_epoch_49.hdf5.
• cv (OPTIONAL, default=None)
  • Folds of cross-validation to use for model validation. If None, no cross-validation is performed.
• window (OPTIONAL, default=0)
  • Size of window to use for enhancing training data by sliding-windows. If 0, all sequences are padded to the length of the longest sequence in the data set.
• step (OPTIONAL, default=1)
  • Step size to move the sliding window or the prediction target
• target (OPTIONAL, default=all)
  • whether to learn all proceeding characters or just the last the single next one in sequence
• target (OPTIONAL, default='all')
  • whether to learn all proceeding characters or just the last 'one' in sequence
• padlen (OPTIONAL, default=0)
  • number of tailing padding spaces to add to the sequences. If 0, sequences are padded to the length of the longest sequence in the dataset.
• refs, (OPTIONAL, default=True
  • whether reference sequence sets should be generated for the analysis

python LSTM_peptides.py --mode pretrain --name train100 --dataset new_sequences.csv --layers 2 --neurons 64 --epochs 100

python LSTM_peptides.py --mode sample --name testsample --modfile pretrained_model/checkpoint/model_epoch_99.hdf5 --sample 100

python LSTM_peptides.py --mode finetune --name finetune10 --dataset finetune_set.csv --modfile pretrained_model/checkpoint/model_epoch_99.hdf5 --epochs 10

When using this code for any publication, please cite the following article:

A. T. Müller, J. A. Hiss, G. Schneider, "Recurrent Neural Network Model for Constructive Peptide Design" J. Chem. Inf . Model. 2018, DOI: 10.1021/acs.jcim.7b00414.