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Graduate Student Seminar Series – Suyue Lyu
September 30 @ 5:55 pm - 6:10 pm EDT
Graduate Student Seminar Series
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Location: HS610 – 155 College St, Room 610
Presentation Title: Variational autoencoder for design of synthetic viral vector serotypes
Abstract: Recent, rapid advances in deep generative models for protein design have focused on small proteins with lots of data. Such models perform poorly on large proteins with limited natural sequences, for instance, the capsid protein of adenoviruses and adeno-associated virus, which are common delivery vehicles for gene therapy. Generating synthetic viral vector serotypes could overcome the potent pre-existing immune responses that most gene therapy recipients exhibit—a consequence of previous environmental exposure. We present a variational autoencoder (ProteinVAE) that can generate synthetic viral vector serotypes without epitopes for pre-existing neutralizing antibodies. A pre-trained protein language model was incorporated into the encoder to improve data efficiency, and deconvolution-based upsampling was used for decoding to avoid degenerate repetition seen in long protein sequence generation. ProteinVAE is a compact generative model with just 12.4 million parameters and was efficiently trained on the limited natural sequences. Viral protein sequences generated were used to produce structures with thermodynamic stability and viral assembly capability indistinguishable from natural vector counterparts. ProteinVAE can be used to generate a broad range of synthetic serotype sequences without epitopes for pre-existing neutralizing antibodies in the human population, effectively addressing one of the major challenges of gene therapy. It could be used more broadly to generate different types of viral vector, and any large, therapeutically valuable proteins, where available data are sparse.
Supervisor Name: Michael Garton
Year of Study: 3
Program of Study: PhD
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