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The ability to predict the evolution of a pathogen would significantlyimprove the ability to control, prevent, and treat disease. Despite significantprogress in other problem spaces, deep learning has yet to contribute to theissue of predicting mutations of evolving populations. To address this gap, wedeveloped a novel machine learning framework using generative adversarialnetworks (GANs) with recurrent neural networks (RNNs) to accurately predictgenetic mutations and evolution of future biological populations. Using ageneralized time-reversible phylogenetic model of protein evolution withbootstrapped maximum likelihood tree estimation, we trained asequence-to-sequence generator within an adversarial framework, named MutaGAN,to generate complete protein sequences augmented with possible mutations offuture virus populations. Influenza virus sequences were identified as an idealtest case for this deep learning framework because it is a significant humanpathogen with new strains emerging annually and global surveillance effortshave generated a large amount of publicly available data from the NationalCenter for Biotechnology Information's (NCBI) Influenza Virus Resource (IVR).MutaGAN generated "child" sequences from a given "parent" protein sequence witha median Levenshtein distance of 2.00 amino acids. Additionally, the generatorwas able to augment the majority of parent proteins with at least one mutationidentified within the global influenza virus population. These resultsdemonstrate the power of the MutaGAN framework to aid in pathogen forecastingwith implications for broad utility in evolutionary prediction for any proteinpopulation.

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