Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences

Abstract

With the advent of deep learning techniques for text generation, comes the possibility of generating fully simulated or synthetic genomes. For this study, the dataset of interest is that of coronaviruses. Coronaviridae are a family of positive-sense RNA viruses capable of infecting humans and animals. These viruses usually cause mild to moderate upper respiratory tract infection; however, they can also cause more severe symptoms, gastrointestinal and central nervous system diseases. The viruses are capable of flexibly adapting to new environments, hence health threats from coronavirus are constant and long-term. Immunogenic spike proteins are glycoproteins found on the surface of Coronaviridae particles that mediate entry to host cells. The aim of this study was to train deep learning neural networks to produce simulated spike protein sequences, which may be able to aid in knowledge and/or vaccine design by creating alternative possible spike sequences that could arise from zoonotic sources in future. Deep learning recurrent neural networks (RNN) were trained to provide computer-simulated coronavirus spike protein sequences in the style of previously known sequences and examine their characteristics. The deep generative model was created as a recurrent neural network employing text embedding and gated recurrent unit layers in TensorFlow Keras. Training used a dataset of alpha, beta, gamma, and delta coronavirus spike sequences. In a set of 100 simulated sequences, all 100 had most significant BLAST matches to Spike proteins in searches against NCBI non-redundant dataset (NR) and possessed the expected Pfam domain matches. Simulated sequences from the neural network may be able to guide us with future prospective targets for vaccine discovery in advance of a potential novel zoonosis.