An artificial intelligence (AI) tool that optimizes the gene sequences found in mRNA vaccines could help to create jabs with greater potency and stability that could be deployed across the globe.

The tangled history of mRNA vaccines

Developed by scientists at the California division of Baidu Research, an AI company based in Beijing, the software borrows techniques from computational linguistics to design mRNA sequences with shapes and structures more intricate than those used in current vaccines. This enables the genetic material to persist for longer than usual. The more stable the mRNA that’s delivered to a person’s cells, the more antigens are produced by the protein-making machinery in that person’s body. This, in turn, leads to a rise in protective antibodies, theoretically leaving immunized individuals better equipped to fend off infectious diseases.

What’s more, the enhanced structural complexity of the mRNA offers improved protection against vaccine degradation. During the COVID-19 pandemic, mRNA-based shots against the coronavirus SARS-CoV-2 famously had to be transported and kept at temperatures below –15 °C to maintain their stability . This limited their distribution in resource-poor regions of the world that lack access to ultracold storage facilities. A more resilient product, optimized by AI, could eliminate the need for cold-chain equipment to handle such jabs.

The new methodology is “remarkable”, says Dave Mauger, a computational RNA biologist who previously worked at Moderna in Cambridge, Massachusetts, a maker of mRNA vaccines. “The computational efficiency is really impressive and more sophisticated than anything that has come before.”

Linear thinking

Vaccine developers already commonly adjust mRNA sequences to align with cells’ preferences for certain genetic instructions over others. This process, known as codon optimization, leads to more-efficient protein production. The Baidu tool takes this a step further, ensuring that the mRNA — usually a single-stranded molecule — loops back on itself to create double-stranded segments that are more rigid (see ‘Design optimization’).

Known as LinearDesign, the tool takes just minutes to run on a desktop computer. In validation tests, it has yielded vaccines that, when evaluated in mice, triggered antibody responses up to 128 times greater than those mounted after immunization with more conventional, codon-optimized vaccines. The algorithm also helped to extend the shelf stability of vaccine designs up to sixfold in standard test-tube assays performed at body temperature.

“It’s a tremendous improvement,” says Yujian Zhang, former head of mRNA technology at StemiRNA Therapeutics in Shanghai, China, who led the experimental-validation studies.

So far, Zhang and his colleagues have tested LinearDesign-enhanced vaccines against only COVID-19 and shingles in mice. But the technique should prove useful when designing mRNA vaccines against any disease, says Liang Huang, a former Baidu scientist who spearheaded the tool’s creation. It should also help in mRNA-based therapeutics, says Huang, who is now a computational biologist at Oregon State University in Corvallis.

The researchers reported their findings on 2 May in Nature ¹ .

Optimal solutions

Already, the tool has been used to optimize at least one authorized vaccine: a COVID-19 shot from StemiRNA, called SW-BIC-213, that won approval for emergency use in Laos late last year . Under a licensing agreement established in 2021, the French pharma giant Sanofi has been using LinearDesign in its own experimental mRNA products, too.

Trial settles debate over best design for mRNA in COVID vaccines

Executives at both companies stress that many design features factor into the performance of their vaccine candidates. But LinearDesign is “certainly one type of algorithm that can help with this”, says Sanofi’s Frank DeRosa, head of research and biomarkers at the company’s mRNA Center of Excellence.

Another was reported last year. A team led by Rhiju Das, a computational biologist at Stanford School of Medicine in California, demonstrated that even greater protein expression can be eked out of mRNA — in cultured human cells at least — if certain loop patterns are taken out of their strands, even when such changes loosen the overall rigidity of the molecule ² .

That suggests that alternative algorithms might be preferable, says theoretical chemist Hannah Wayment-Steele, a former member of Das’s team who is now at Brandeis University in Waltham, Massachusetts. Or, it suggests that manual fine-tuning of LinearDesign-optimized mRNA could lead to even better vaccine sequences.

But according to David Mathews, a computational RNA biologist at the University of Rochester Medical Center in New York, LinearDesign can do the bulk of the heavy lifting. “It gets people in the right ballpark to start doing any optimization,” he says. Mathews helped develop the algorithm and is a co-founder, along with Huang, of Coderna.ai, a start-up based in Sunnyvale, California, that is developing the software further. Their first task has been updating the platform to account for the types of chemical modification found in most approved and experimental mRNA vaccines; LinearDesign, in its current form, is based on an unmodified mRNA platform that has fallen out of favour among most vaccine developers .

A structured approach

But mouse studies and cell experiments are one thing. Human trials are another. Given that the immune system has evolved to recognize certain RNA structures as foreign — especially the twisted ladder shapes within many viruses that encode their genomes as double-stranded RNA — some researchers worry that an optimization algorithm such as LinearDesign could end up creating vaccine sequences that spur harmful immune reactions in people.

“That’s kind of a liability,” says Anna Blakney, an RNA bioengineer at the University of British Columbia in Vancouver, Canada, who was not involved in the study.

Early results from human clinical trials involving StemiRNA’s SW-BIC-213 suggest the extra structure is not a problem, however. In small booster trials reported so far, the shot’s side effects have proved no worse than those reported with other mRNA-based COVID-19 vaccines ³ . But as Blakney points out: “We’ll learn more about that in the coming years.”

article_text: An artificial intelligence (AI) tool that optimizes the gene sequences found in mRNA vaccines could help to create jabs with greater potency and stability that could be deployed across the globe.

The tangled history of mRNA vaccines

Developed by scientists at the California division of Baidu Research, an AI company based in Beijing, the software borrows techniques from computational linguistics to design mRNA sequences with shapes and structures more intricate than those used in current vaccines. This enables the genetic material to persist for longer than usual. The more stable the mRNA that’s delivered to a person’s cells, the more antigens are produced by the protein-making machinery in that person’s body. This, in turn, leads to a rise in protective antibodies, theoretically leaving immunized individuals better equipped to fend off infectious diseases. What’s more, the enhanced structural complexity of the mRNA offers improved protection against vaccine degradation. During the COVID-19 pandemic, mRNA-based shots against the coronavirus SARS-CoV-2 famously had to be transported and kept at temperatures below –15 °C to maintain their stability. This limited their distribution in resource-poor regions of the world that lack access to ultracold storage facilities. A more resilient product, optimized by AI, could eliminate the need for cold-chain equipment to handle such jabs. The new methodology is “remarkable”, says Dave Mauger, a computational RNA biologist who previously worked at Moderna in Cambridge, Massachusetts, a maker of mRNA vaccines. “The computational efficiency is really impressive and more sophisticated than anything that has come before.” Vaccine developers already commonly adjust mRNA sequences to align with cells’ preferences for certain genetic instructions over others. This process, known as codon optimization, leads to more-efficient protein production. The Baidu tool takes this a step further, ensuring that the mRNA — usually a single-stranded molecule — loops back on itself to create double-stranded segments that are more rigid (see ‘Design optimization’). Known as LinearDesign, the tool takes just minutes to run on a desktop computer. In validation tests, it has yielded vaccines that, when evaluated in mice, triggered antibody responses up to 128 times greater than those mounted after immunization with more conventional, codon-optimized vaccines. The algorithm also helped to extend the shelf stability of vaccine designs up to sixfold in standard test-tube assays performed at body temperature. “It’s a tremendous improvement,” says Yujian Zhang, former head of mRNA technology at StemiRNA Therapeutics in Shanghai, China, who led the experimental-validation studies. So far, Zhang and his colleagues have tested LinearDesign-enhanced vaccines against only COVID-19 and shingles in mice. But the technique should prove useful when designing mRNA vaccines against any disease, says Liang Huang, a former Baidu scientist who spearheaded the tool’s creation. It should also help in mRNA-based therapeutics, says Huang, who is now a computational biologist at Oregon State University in Corvallis. The researchers reported their findings on 2 May in Nature1. Already, the tool has been used to optimize at least one authorized vaccine: a COVID-19 shot from StemiRNA, called SW-BIC-213, that won approval for emergency use in Laos late last year. Under a licensing agreement established in 2021, the French pharma giant Sanofi has been using LinearDesign in its own experimental mRNA products, too.

Trial settles debate over best design for mRNA in COVID vaccines

Executives at both companies stress that many design features factor into the performance of their vaccine candidates. But LinearDesign is “certainly one type of algorithm that can help with this”, says Sanofi’s Frank DeRosa, head of research and biomarkers at the company’s mRNA Center of Excellence. Another was reported last year. A team led by Rhiju Das, a computational biologist at Stanford School of Medicine in California, demonstrated that even greater protein expression can be eked out of mRNA — in cultured human cells at least — if certain loop patterns are taken out of their strands, even when such changes loosen the overall rigidity of the molecule2. That suggests that alternative algorithms might be preferable, says theoretical chemist Hannah Wayment-Steele, a former member of Das’s team who is now at Brandeis University in Waltham, Massachusetts. Or, it suggests that manual fine-tuning of LinearDesign-optimized mRNA could lead to even better vaccine sequences. But according to David Mathews, a computational RNA biologist at the University of Rochester Medical Center in New York, LinearDesign can do the bulk of the heavy lifting. “It gets people in the right ballpark to start doing any optimization,” he says. Mathews helped develop the algorithm and is a co-founder, along with Huang, of Coderna.ai, a start-up based in Sunnyvale, California, that is developing the software further. Their first task has been updating the platform to account for the types of chemical modification found in most approved and experimental mRNA vaccines; LinearDesign, in its current form, is based on an unmodified mRNA platform that has fallen out of favour among most vaccine developers. But mouse studies and cell experiments are one thing. Human trials are another. Given that the immune system has evolved to recognize certain RNA structures as foreign — especially the twisted ladder shapes within many viruses that encode their genomes as double-stranded RNA — some researchers worry that an optimization algorithm such as LinearDesign could end up creating vaccine sequences that spur harmful immune reactions in people. “That’s kind of a liability,” says Anna Blakney, an RNA bioengineer at the University of British Columbia in Vancouver, Canada, who was not involved in the study. Early results from human clinical trials involving StemiRNA’s SW-BIC-213 suggest the extra structure is not a problem, however. In small booster trials reported so far, the shot’s side effects have proved no worse than those reported with other mRNA-based COVID-19 vaccines3. But as Blakney points out: “We’ll learn more about that in the coming years.” vocabulary:

{'Artificial Intelligence': '人工智能：指通过计算机程序模拟人类智能的一种技术', 'Computational Linguistics': '计算语言学：是计算机科学和语言学的交叉学科，研究如何使用计算机处理语言', 'Codon Optimization': '密码子优化：是一种技术，用于改变基因组中的密码子序列，以改善基因表达', 'LinearDesign': '线性设计：是一种AI工具，可以优化mRNA疫苗中发现的基因序列，以创造更强效和稳定的疫苗', 'Antigens': '抗原：是一种能够引起免疫反应的物质，可以被免疫系统识别', 'Antibodies': '抗体：是一种由免疫系统产生的蛋白质，可以识别和结合抗原', 'Cold-chain': '冷链：指在运输、储存和销售过程中，控制食品温度的一种技术', 'Codon': '密码子：是DNA或RNA中的三个连续碱基，它们编码一个特定的氨基酸', 'Rigidity': '刚度：指物体的硬度，即物体的弹性变形能力', 'Therapeutics': '治疗：指用于治疗疾病的药物或技术', 'Validation': '验证：指确认某种方法或技术是否有效的过程', 'Assays': '测定：指用于测定某种物质的含量或活性的实验', 'Booster': '增强剂：指用于增强免疫力的物质', 'Bioengineer': '生物工程师：指研究和开发生物技术的专业人员', 'Computational': '计算：指使用计算机程序来解决问题的过程', 'Theoretical': '理论：指基于假设和推理的研究方法', 'Bioinformatics': '生物信息学：指研究和分析生物数据的科学', 'Biomarkers': '生物标志物：指可以用于诊断、预测、监测和治疗疾病的生物学标志物', 'Experimental': '实验：指用于检验假设的实验方法', 'Immunized': '免疫：指使机体具有抵抗病原体的能力', 'Immune': '免疫：指机体具有抵抗病原体的能力', 'Liability': '责任：指一方对另一方承担的义务或责任'} readguide:

{'reading_guide': '本文讲述了一种人工智能（AI）工具，它可以优化mRNA疫苗中发现的基因序列，从而帮助创建更有效、更稳定的疫苗，可以在全球范围内部署。该工具采用计算语言学技术来设计具有比当前疫苗更复杂形状和结构的mRNA序列，从而使遗传物质比通常更长时间存在。此外，mRNA的增强结构复杂性可提高疫苗降解的保护性。该工具可以在台式电脑上几分钟内运行，并在小鼠实验中产生比传统编码优化疫苗更强的抗体反应，并可以在标准试管实验中将疫苗设计的货架稳定性提高6倍。该工具已袞助至少一种授权疫苗：去年底在老挝获得紧急使用批准的StemiRNA的SW-BIC-213疫苗。此外，还讨论了mRNA疫苗最佳设计的争论，以及可能存在的安全风险。'} long_sentences:

{'sentence 1': 'Developed by scientists at the California division of Baidu Research, an AI company based in Beijing, the software borrows techniques from computational linguistics to design mRNA sequences with shapes and structures more intricate than those used in current vaccines.', 'sentence 2': 'Early results from human clinical trials involving StemiRNA’s SW-BIC-213 suggest the extra structure is not a problem, however. In small booster trials reported so far, the shot’s side effects have proved no worse than those reported with other mRNA-based COVID-19 vaccines3.'}

Sentence 1: 开发这款软件的是来自北京百度研究院加州分部的科学家，该软件借鉴了计算语言学的技术，用于设计比当前疫苗中使用的更复杂的mRNA序列形状和结构。

句子1的句子结构：主语+谓语+宾语+定语+状语+宾语+定语+宾语。

句子1的语义：百度研究院加州分部的科学家开发了一款软件，该软件借鉴了计算语言学的技术，用于设计比当前疫苗中使用的更复杂的mRNA序列形状和结构。

Sentence 2: 早期的人类临床试验结果表明，StemiRNA的SW-BIC-213疫苗的额外结构并不是问题，小型助推试验中，该疫苗的副作用没有比其他基于mRNA的COVID-19疫苗报告的更糟糕。

句子2的句子结构：主语+谓语+宾语+定语+状语+宾语+定语+宾语+定语+宾语。

句子2的语义：早期的人类临床试验结果表明，StemiRNA的SW-BIC-213疫苗的额外结构并不是问题，小型助推试验中，该疫苗的副作用没有比其他基于mRNA的COVID-19疫苗报告的更糟糕。