TargetP-2.0: Detecting Sequence Signals in Targeting Peptides Using Deep Learning

Synopsis

Repository with the code used to train and test the tool TargetP-2.0.

Authors

J. J. Almagro Armenteros, M. Salvatore, O. Emanuelsson, O. Winther, G. von Heijne, A. Elofsson, H. Nielsen.

Software requirements

Python 3 and Tensorflow 1.7 where used to train and test the model. Additional packages are numpy (processing of the data) and sklearn (metrics).

Data

The protein sequences are encoded in BLOSUM62 and trimmed up to 200 amino acids. The sequences are stored in one npz file named targetp_data.npz in the data folder. The file contains the following arrays:

• Protein sequences (x) encoded in BLOSUM62 and trimmed up to 200 amino acids. Sequences smaller than 200 amino acids are padded.
• Peptide types (y_type) that the input protein can be classified as. They can be noTP 0, SP 1, mTP 2, cTP 3 or luTP 4.
• Cleavage site (y_cs) is the position where the sorting signal is cleaved. This is enconded as a zero vector of length 200 with 1 in the cleavage site position.
• Protein organism (org) defines whether the protein is from plant 1 or non-plant 0.
• Sequence length (len_seq) of the proteins, being the maximum length 200.
• Partition assignment (fold) of each example. The protein data was divided in 5 partitions based on their sequence similarity. This vector contains the partition that each protein belongs to, which is used in the cross-validation procedure.
• Accession number (ids) of the proteins.

Training

The training is performed running the script train.py. This is a minimal example:

python train.py -d targetp_data.npz

By default, the training will run on the CPU. To select the GPU to run the training, for example GPU 0, use:

python train.py -d targetp_data.npz -g 0

Testing

Once the training is finished run the test.py script to get the final performance of the model. It is necessary to define the folder where the trained models have been saved. python test.py -d targetp_data.npz -m saved_models