 [2305.11194] Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2




























  








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Quantitative Biology > Biomolecules


arXiv:2305.11194 (q-bio)
    

COVID-19 e-print
Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.





  [Submitted on 18 May 2023]
Title:Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2
Authors:Aryo Pradipta Gema, Michał Kobiela, Achille Fraisse, Ajitha Rajan, Diego A. Oyarzún, Javier Antonio Alfaro View a PDF of the paper titled Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2, by Aryo Pradipta Gema and 5 other authors
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Abstract:The SARS-CoV-2 pandemic has emphasised the importance of developing a universal vaccine that can protect against current and future variants of the virus. The present study proposes a novel conditional protein Language Model architecture, called Vaxformer, which is designed to produce natural-looking antigenicity-controlled SARS-CoV-2 spike proteins. We evaluate the generated protein sequences of the Vaxformer model using DDGun protein stability measure, netMHCpan antigenicity score, and a structure fidelity score with AlphaFold to gauge its viability for vaccine development. Our results show that Vaxformer outperforms the existing state-of-the-art Conditional Variational Autoencoder model to generate antigenicity-controlled SARS-CoV-2 spike proteins. These findings suggest promising opportunities for conditional Transformer models to expand our understanding of vaccine design and their role in mitigating global health challenges. The code used in this study is available at this https URL .
    



Subjects:

Biomolecules (q-bio.BM); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)

Cite as:
arXiv:2305.11194 [q-bio.BM]


 
(or 
arXiv:2305.11194v1 [q-bio.BM] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2305.11194



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                arXiv-issued DOI via DataCite
              







Submission history From: Aryo Gema [view email]       [v1]
        Thu, 18 May 2023 13:36:57 UTC (3,595 KB)



 

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