Affiliation:
1. Johns Hopkins Applied Physics Laboratory , 11100 Johns Hopkins Rd., Laurel, MD 20723, USA
Abstract
AbstractThe ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Machine learning, however, is yet to be used to predict the evolutionary progeny of a virus. To address this gap, we developed a novel machine learning framework, named MutaGAN, using generative adversarial networks with sequence-to-sequence, recurrent neural networks generator to accurately predict genetic mutations and evolution of future biological populations. MutaGAN was trained using a generalized time-reversible phylogenetic model of protein evolution with maximum likelihood tree estimation. MutaGAN was applied to influenza virus sequences because influenza evolves quickly and there is a large amount of publicly available data from the National Center for Biotechnology Information’s Influenza Virus Resource. MutaGAN generated ‘child’ sequences from a given ‘parent’ protein sequence with a median Levenshtein distance of 4.00 amino acids. Additionally, the generator was able to generate sequences that contained at least one known mutation identified within the global influenza virus population for 72.8 per cent of parent sequences. These results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.
Funder
National Institute of Allergy and Infectious Diseases
JHUAPL Janney Program
Publisher
Oxford University Press (OUP)
Reference102 articles.
1. Tensorflow: A system for large-scale machine learning;Abadi,2016
2. Predicting the Sequence Specificities of DNA- and RNA-Binding Proteins by Deep Learning;Alipanahi;Nature Biotechnology,2015
3. Generative Modeling for Protein Structures;Anand;Advances in Neural Information Processing Systems,2018
4. Wasserstein Generative Adversarial Networks;Arjovsky,2017
5. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics;Asgari;PLoS One,2015