@inproceedings{
li2023codonbert,
title={Codon{BERT}: Large Language Models for m{RNA} design and optimization},
author={Sizhen Li and Saeed Moayedpour and Ruijiang Li and Michael Bailey and Saleh Riahi and Milad Miladi and Jacob Miner and Dinghai Zheng and Jun Wang and Akshay Balsubramani and Khang Tran and Minnie and Monica Wu and Xiaobo Gu and Ryan Clinton and Carla Asquith and Joseph Skaleski and Lianne Boeglin and Sudha Chivukula and Anusha Dias and Fernando Ulloa Montoya and Vikram Agarwal and Ziv Bar-Joseph and Sven Jager},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=46okCAggF5}
}
mRNA based vaccines and therapeutics are gaining popularity and usage across a wide range of conditions. One of the critical issues when designing such mRNAs is sequence optimization. Even small proteins or peptides can be encoded by an enormously large number of mRNAs. The actual mRNA sequence can have a large impact on several properties including expression, stability, immunogenicity, and more. To enable the selection of an optimal sequence, we developed CodonBERT, a large language model (LLM) for mRNAs. Unlike prior models, CodonBERT uses codons as inputs which enables it to learn better representations. CodonBERT was trained using more than 10 million mRNA sequences from a diverse set of organisms. The resulting model captures important biological concepts. CodonBERT can also be extended to perform prediction tasks for various mRNA properties. CodonBERT outperforms previous mRNA prediction methods including on a new flu vaccine dataset.
CodonBERT: Large Language Models for mRNA design and optimization
Sizhen Li Saeed Moayedpour Ruijiang Li Michael Bailey Saleh Riahi Milad Miladi Jacob Miner Dinghai Zheng Jun Wang Akshay Balsubramani Khang Tran Minnie Monica Wu Xiaobo Gu Ryan Clinton Carla Asquith Joseph Skaleski Lianne Boeglin Sudha Chivukula Anusha Dias Fernando Ulloa Montoya Vikram Agarwal Ziv Bar-Joseph Sven Jager