Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 58, Issue 2, Pages 472-479Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.7b00414
Keywords
-
Categories
Funding
- Swiss National Science Foundation [200021_157190]
- Swiss National Science Foundation (SNF) [200021_157190] Funding Source: Swiss National Science Foundation (SNF)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available