MutaGAN: A Seq2seq GAN Framework to Predict Mutations of Evolving Protein Populations

The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Despite significant progress in other problem spaces, deep learning has yet to contribute to the issue of predicting mutations of evolving populations. To address this g...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Berman, Daniel S, Howser, Craig, Mehoke, Thomas, Evans, Jared D
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.08.2020
Subjects
Amino acids
Case depth
Deep learning
Disease control
Evolution
Influenza
Machine learning
Maximum likelihood estimation
Mutation
Pathogens
Populations
Proteins
Recurrent neural networks
Stability
Viruses
Online AccessGet full text

Cover

More Information
Summary:The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Despite significant progress in other problem spaces, deep learning has yet to contribute to the issue of predicting mutations of evolving populations. To address this gap, we developed a novel machine learning framework using generative adversarial networks (GANs) with recurrent neural networks (RNNs) to accurately predict genetic mutations and evolution of future biological populations. Using a generalized time-reversible phylogenetic model of protein evolution with bootstrapped maximum likelihood tree estimation, we trained a sequence-to-sequence generator within an adversarial framework, named MutaGAN, to generate complete protein sequences augmented with possible mutations of future virus populations. Influenza virus sequences were identified as an ideal test case for this deep learning framework because it is a significant human pathogen with new strains emerging annually and global surveillance efforts have generated a large amount of publicly available data from the National Center for Biotechnology Information's (NCBI) Influenza Virus Resource (IVR). MutaGAN generated "child" sequences from a given "parent" protein sequence with a median Levenshtein distance of 2.00 amino acids. Additionally, the generator was able to augment the majority of parent proteins with at least one mutation identified within the global influenza virus population. 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.
ISSN:2331-8422