Bespoke

Gene

Context (archaeology)

Generative model

Biology

Construct (python library)

Peptide

Biochemistry

Computer science

Computational biology

Paleontology

Generative grammar

Sequence (biology)

Political science

Artificial intelligence

Programming language

Deep learning

Artificial neural network

Peptide sequence

Recurrent neural network

Machine learning

Journal of Chemical Information and Modeling

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.

Recurrent Neural Network Model for Constructive Peptide Design