The current server. New in this version:
- Deep learning:
TargetP 2.0 is based on convolutional and recurrent (LSTM) neural
networks with a multi-attention layer.
The deep recurrent neural network architecture is better suited to
recognizing sequence motifs of varying length, such as signal
or transit peptides, than
traditional feed-forward neural networks (as used in TargetP 1).
- Thylakoid lumen proteins:
TargetP 2.0 is now able to predict thylakoid luminal transit peptides (luTPs),
which are composed of a chloroplast transit peptide (cTP) followed by
a second peptide similar to a bacterial signal peptide.
The original server. Based on feed-forward neural networks.