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Natural Language Processing for Language of Life (mRNA vaccine design)
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- Author / Creator
- Pathak, Shashank
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The COVID-19 pandemic accelerated the development of mRNA vaccines, yet iden- tifying the optimal mRNA sequence for human use, particularly for the SARS-CoV- 2 spike protein, remains challenging. This thesis focuses on optimizing the open reading frame (ORF), a crucial mRNA component composed of codons—triplets of nucleotides coding for amino acids. We introduce a novel ‘valid-codon’ masking strat- egy to streamline codon-to-amino acid mapping within the target protein sequence. This approach was competitive to the ‘codon-box’ method, which groups codons with identical nucleotide compositions. Our findings show that ‘valid-codon’ per- forms comparably to ‘codon-box’ in optimizing ORF sequences for gene expression. By integrating the masking strategy into a supervised fine-tuning (SFT) process us- ing the pre-trained ProtBert model, we further optimize the ORF for humans for the SARS-CoV-2 spike protein. Results indicate that our fine-tuned models surpass the ORF sequences used in Moderna and Pfizer vaccines in terms of gene expression and stability.
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- Subjects / Keywords
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- Graduation date
- Fall 2024
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.