CodonBERT large language model for mRNA vaccines.

IF 6.2 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Genome research Pub Date : 2024-08-20 DOI:10.1101/gr.278870.123

Sizhen Li, Saeed Moayedpour, Ruijiang Li, Michael Bailey, Saleh Riahi, Lorenzo Kogler-Anele, Milad Miladi, Jacob Miner, Fabien Pertuy, Dinghai Zheng, Jun Wang, Akshay Balsubramani, Khang Tran, Minnie Zacharia, Monica Wu, Xiaobo Gu, Ryan Clinton, Carla Asquith, Joseph Skaleski, Lianne Boeglin, Sudha Chivukula, Anusha Dias, Tod Strugnell, Fernando Ulloa Montoya, Vikram Agarwal, Ziv Bar-Joseph, Sven Jager

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Abstract

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 data set.

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用于 mRNA 疫苗的 CodonBert 大语言模型。

以 mRNA 为基础的疫苗和疗法越来越受到人们的青睐，并被广泛应用于各种疾病。设计此类 mRNA 的关键问题之一是序列优化。即使是很小的蛋白质或肽也可以由大量的 mRNA 编码。实际的 mRNA 序列会对包括表达、稳定性、免疫原性等在内的多种特性产生重大影响。为了能够选择最佳序列，我们开发了用于 mRNA 的大语言模型（LLM）--CodonBERT。与之前的模型不同，CodonBERT 使用密码子作为输入，这使它能够学习更好的表征。CodonBERT 使用来自不同生物体的 1000 多万条 mRNA 序列进行训练。由此产生的模型捕捉到了重要的生物学概念。CodonBERT 还可扩展用于执行各种 mRNA 属性的预测任务。CodonBERT 的表现优于之前的 mRNA 预测方法，包括在一个新的流感疫苗数据集上的表现。

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来源期刊

Genome research 生物-生化与分子生物学

CiteScore

12.40

自引率

1.40%

发文量

140

审稿时长

6 months

期刊介绍： Launched in 1995, Genome Research is an international, continuously published, peer-reviewed journal that focuses on research that provides novel insights into the genome biology of all organisms, including advances in genomic medicine. Among the topics considered by the journal are genome structure and function, comparative genomics, molecular evolution, genome-scale quantitative and population genetics, proteomics, epigenomics, and systems biology. The journal also features exciting gene discoveries and reports of cutting-edge computational biology and high-throughput methodologies. New data in these areas are published as research papers, or methods and resource reports that provide novel information on technologies or tools that will be of interest to a broad readership. Complete data sets are presented electronically on the journal''s web site where appropriate. The journal also provides Reviews, Perspectives, and Insight/Outlook articles, which present commentary on the latest advances published both here and elsewhere, placing such progress in its broader biological context.