Deep generative models to generate hypothetical SARS-CoV-2 spike sequences PandoGen DeepLearning SARSCoV2 SpikeProtein GenerativeModels ProteinSequences PandemicPreparedness biorxivpreprint UofIllinois
By Dr. Sanchari Sinha Dutta, Ph.D.May 14 2023 Scientists at the University of Illinois at Urbana-Champaign have developed deep generative models to predict undiscovered sequences of the severe acute respiratory syndrome coronavirus 2 spike protein. These hypothetical sequences could be useful for future pandemic preparedness. The study is currently available on the bioRxiv* preprint server.
Protein Language Models are based on LLM designs and can model biological sequences and generate samples with interesting properties. Many therapeutic and preventive interventions targeting the spike protein have been developed during the coronavirus disease 2019 pandemic, including therapeutic monoclonal antibodies and COVID-19 vaccines. Thus, advance knowledge of future spike protein sequences would be helpful for developing novel variant-specific vaccines and monoclonal antibodies.
The comparison of model-generated sample sequences with GISAID-derived sequences revealed PandoGen is capable of generating a high fraction of real sequences. The model also showed proficiency in generating novel sequences associated with GISAID cases.
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