Recurrent Neural Network Model for Constructive Peptide Design

We
present a generative long short-term memory (LSTM) recurrent
neural network (RNN) for combinatorial de novo peptide design. RNN
models capture patterns in sequential data and generate new data instances
from the learned context. Amino acid sequences represent a suitable
input for these machine-learning models. Generative models trained
on peptide sequences could therefore facilitate the design of bespoke
peptide libraries. We trained RNNs with LSTM units on pattern recognition
of helical antimicrobial peptides and used the resulting model for
de novo sequence generation. Of these sequences, 82% were predicted
to be active antimicrobial peptides compared to 65% of randomly sampled
sequences with the same amino acid distribution as the training set.
The generated sequences also lie closer to the training data than
manually designed amphipathic helices. The results of this study showcase
the ability of LSTM RNNs to construct new amino acid sequences within
the applicability domain of the model and motivate their prospective
application to peptide and protein design without the need for the
exhaustive enumeration of sequence libraries.