内容来源：https://www.sciencenews.org/article/ai-genome-bacteria-phage

内容总结：

近日，科研团队首次利用人工智能技术成功设计出完整的病毒基因组，这一突破为应对耐药菌感染提供了全新思路。相关预印本论文已于9月17日在生物医学预印本平台bioRxiv.org发布。

研究团队采用名为Evo 1和Evo 2的两种人工智能模型，以1977年完成测序的ΦX174噬菌体为参照模板，成功设计出16种能有效攻击大肠杆菌的噬菌体基因组。实验显示，这些AI设计的噬菌体组合不仅能有效抑制实验室培养皿中的大肠杆菌生长，还对三种耐药性大肠杆菌菌株展现出显著杀伤效果。

斯坦福大学计算生物学家布莱恩·希指出，这是人工智能首次成功生成完整基因组，标志着该技术向生命体设计领域迈出关键一步。相较于传统仅设计单个基因或蛋白质的方法，构建完整基因组需要协调众多基因与蛋白质的协同运作，技术难度显著提升。

为确保生物安全，研究团队在模型训练阶段刻意避开了所有人类致病病毒数据。约翰斯·霍普金斯大学微生物学家金伯利·戴维斯评价称，这项技术有望快速匹配特定菌株的噬菌体，为临床治疗耐药菌感染赢得宝贵时间，但必须建立严格监管机制，确保AI设计的噬菌体不会对其他微生物群系造成损害。

展望未来，AI设计生物体的技术还可应用于加速微生物制造流程，如优化抗生素生产工艺、培育降解塑料的微生物等。不过研究人员强调，人类基因组规模是ΦX174噬菌体的50万倍以上，要实现复杂生命体的精准设计仍面临巨大挑战。

中文翻译：

人工智能的能力已不仅限于撰写日常邮件。9月17日发布于bioRxiv.org的研究报告显示，两种人工智能模型成功设计出16种能在培养皿中攻击大肠杆菌的病毒基因图谱。由这些AI设计的噬菌体混合制剂成功抑制了抗病毒大肠杆菌菌株的生长，表明该技术有望帮助科学家设计对抗耐药性微生物感染的疗法。这项研究尚未经过同行评审。

斯坦福大学与帕洛阿尔托弧研究所的计算生物学家布莱恩·希指出，这是人工智能首次成功生成完整基因组。尽管病毒是否属于生命体仍存争议，但该研究标志着人类向利用AI设计生命体迈出了关键一步。

目前AI模型已能设计单个基因和蛋白质，但希强调，从头构建完整基因组需要协调众多基因与蛋白质的协同作用，其复杂性远超前者。研究团队利用自行开发的"进化1号"和"进化2号"AI模型，尝试设计杀菌病毒基因组。这些模型以噬菌体基因组中数十亿对ACGT碱基对进行训练，其原理类似ChatGPT通过小说和网络帖文学习语言。团队以1977年完成首例DNA测序的ΦX174噬菌体为参照，引导AI设计类似基因组。

选择ΦX174因其研究资料完备，"若AI对噬菌体产生新颖突变，我们能立即识别其创新性"。此外噬菌体不感染人类，保障了实验室安全。出于对AI可能设计有害病毒的担忧，团队未采用任何病毒病原体样本进行训练。

两个AI模型生成约300个潜在噬菌体基因组，其中16个成功感染大肠杆菌。部分噬菌体的杀菌速度甚至超越ΦX174。当ΦX174单独无法杀死三种抗噬菌体大肠杆菌时，AI设计的噬菌体组合却能快速进化突破细菌抗性。

约翰斯·霍普金斯大学彭博公共卫生学院微生物学家金伯利·戴维斯认为，这项技术有望帮助开发用于噬菌体疗法的病毒，为抗生素耐药性感染提供新方案。她表示："当急需靶向特定菌株的噬菌体时，利用AI快速生成匹配治疗方案将极具价值"，同时强调"AI生成的噬菌体必须受到严格监管"，需通过全面测试确保其不干扰或损害其他微生物。

希指出，理想的AI设计噬菌体应精准靶向致病菌，保护人体有益菌群，并能持续进化应对细菌抗性。利用AI设计完整生物体还能加速微生物制造流程，如优化抗生素生产或培育降解塑料的微生物。尽管人类基因组规模是ΦX174的五十万倍，希相信AI终将帮助解析更复杂基因组，为疑难杂症开发全新疗法。

英文来源：

Artificial intelligence can dash off more than routine emails. It has now written tiny working genomes.
Two AI models designed the blueprints for 16 viruses capable of attacking Escherichia coli in lab dishes, researchers report September 17 in a paper posted to bioRxiv.org. A mixture of these AI-generated bacteriophages stopped virus-resistant E. coli strains from growing, suggesting that the technique could help scientists design therapies capable of taking on tough-to-treat microbial infections. The work has not yet been peer-reviewed.
It’s the first time that AI has successfully generated an entire genome, says Brian Hie, a computational biologist at Stanford University and the Arc Institute in Palo Alto, Calif. And while it’s debatable whether viruses are alive or not, the work is a step toward using the technology to design living organisms.
AI models have already been used to devise individual genes and proteins. Creating an entire genetic blueprint from scratch, however, adds an extra layer of complexity because numerous genes and proteins need to work together, Hie says.
Hie and colleagues turned to two of their own AI models, called Evo 1 and Evo 2, to see if they could create genomes for bacteria-killing viruses. The models were trained on billions of pairs of the genetic alphabet’s basic units, A, C, G and T’s, from phage genomes the way ChatGPT was trained on novels and internet posts. The team used a bacteriophage called ΦX174 — which in 1977 became the first DNA-based genome ever sequenced — as a guide to help the AI design a similar genome.
Because ΦX174 has been so well-studied, “if the AI was making novel mutations to the phage, we would be able to see how novel they are,” Hie says. What’s more, bacteriophages don’t infect people, so it was safe to work with in the lab. Out of concern that the AI might design viruses that could harm people, the team did not train the models on any examples of viral pathogens.
Evo 1 and Evo 2 generated roughly 300 potential phage genomes. Of those, 16 produced viable viruses that could infect E. coli. Some of the phages even killed E. coli more quickly than ΦX174 did. And although ΦX174 couldn’t kill three phage-resistant strains of E. coli on its own, cocktails of AI-generated phages rapidly evolved to overcome the bacteria’s resistance to infection.
The findings suggest that AI could help researchers develop viruses to use in phage therapy, a potential option to treat antibiotic-resistant bacterial infections. In such cases, “the need to find a phage that targets the bacterial strain would be very urgent,” says Kimberly Davis, a microbiologist at Johns Hopkins Bloomberg School of Public Health who wasn’t involved in the work. “Utilizing AI could be a powerful way of rapidly generating a phage match to treat patients.”
Davis notes that “the use of AI-generated phages would need to be tightly controlled.” For instance, extensive testing could make sure that such phages don’t interact with or harm other microbes.
AI-generated phages would ideally not only kill just one bad type of bacteria while sparing good bacteria that keep people healthy, Hie says, but might also evolve in ways that keep up with virus-resistant bacteria. Using AI to design entire organisms could also speed up microbial manufacturing processes such as antibiotic production or cultivate microbes that degrade plastic.
And AI has the potential to help researchers make sense of genomes that are even more complex and develop new treatments for complicated diseases, Hie says. The human genome is more than half a million times the size of ΦX174’s genome, “so there’s a lot of work to go.”