新的人工智能算法可以有效地提高COVID-19 mRNA疫苗的抗体反应128倍的
![The 新的人工智能算法提高COVID-19 mRNA疫苗的抗体反应128倍的](https://scx1.b-cdn.net/csz/news/800a/2023/new-ai-algorithm-boost-2.jpg)
从百度的一组研究人员研究开发了一种人工智能算法,可以快速设计高度稳定的疫苗COVID-19信使rna序列,以前高不可攀。算法,名叫LinearDesign,代表一个重大飞跃在两种疫苗序列的稳定性和有效性,实现128倍增加COVID-19疫苗的抗体反应。
“这项研究可以将信使rna药物编码应用到更广泛的治疗性蛋白质,如单克隆抗体和抗癌药物,承诺广泛应用和深远的影响,”博士说他张,百度员工软件工程师的研究。
通过与俄勒冈州立大学合作,StemiRNA疗法,罗切斯特大学医学中心,研究“算法优化的mRNA设计提高了稳定和免疫原性”出现在《华尔街日报》自然。
本文揭示了如何解决一个复杂的生物学问题通过一个经典的方法自然语言处理(NLP),使用一个优雅简单的解决方案,用来理解单词和语法。
![a, The combinatorial nature of mRNA design due to codon degeneracy (~10632 mRNA sequences for the Spike protein; taking ~10616 billion years to enumerate). The pink and blue paths represent the wildtype and the optimally stable (i.e., lowest energy) sequences, respectively. b, The vastly different secondary structures between these two sequences, with the former being mostly single-stranded (prone to degradation in red loop regions) while the latter mostly double-stranded. Our algorithm takes just 11 minutes for this optimization. c, An analogy between linguistics (left) and biology (right), where deterministic finite-state automaton (DFA) and lattice parsing from the former were adapted to solve mRNA design. An mRNA DFA (inspired by “word lattice”) compactly encodes all mRNA candidates, which are folded simultaneously by lattice parsing to find the optimal mRNA (Fig. 2). d, 2D visualization of the mRNA design space, with stability on the x-axis and codon optimality on the y-axis. The standard mRNA design method codon optimization improves codon usage (the pink arrow) but is unable to explore the vast high-stability region (left of the dashed line), which is exemplified by the COVID-19 vaccine products of BioNTech-Pfizer (○), Moderna (☆), and CureVac (▷). LinearDesign, by contrast, jointly optimizes stability and codon optimality (the blue curve, with λ being the weight of the latter). By considering other factors, we select seven of our designs (four shown here) for COVID-19 vaccine experiments (Fig. 4), which show substantially enhanced half-life and protein expression, and up to 128 antibody responses over the codon-optimized baseline (H). Experiments on the varicella-zoster virus (VZV) mRNA vaccine (on a different antigen, and with different UTRs) show similar improvements (Fig. 5), confirming the generalizability of LinearDesign. Credit: Baidu Research 新的人工智能算法提高COVID-19 mRNA疫苗的抗体反应128倍的](https://scx1.b-cdn.net/csz/news/800a/2023/new-ai-algorithm-boost.jpg)
信使RNA或信使RNA,疫苗已成为一个革命性的技术发展和潜在的治疗对癌症和其他疾病。作为一个至关重要的信使,携带遗传指令从DNA到细胞的蛋白质合成器,信使rna就可以创建特定的蛋白质在人体各种功能。与众多优点在安全、功效和生产,信使rna已经迅速采用COVID-19疫苗开发的过程。
然而,信使rna的自然不稳定导致蛋白表达不足,削弱了疫苗刺激强烈的免疫反应的能力。这种不稳定性也给存储和传输带来了挑战mRNA疫苗,特别是在发展中国家的资源往往是有限的。
先前的研究已经表明,优化信使rna二级结构的稳定性,当结合最优密码子,导致改善蛋白表达。挑战在于mRNA设计空间,这是令人难以置信的巨大的同义密码子。例如,大约有10632年mrna,可以翻译成SARS-CoV-2突起蛋白相同,呈现方法之前不可逾越的挑战。
尽管NLP和生物学可能乍一看似乎不相关的,这两个领域分享很强的数学关系。在人类的语言,一个句子由单词序列和一个潜在的句法树与名词和动词短语,传达意义。同样,RNA链的核苷酸序列和一个相关的二级结构根据其折叠模式。
![a, Summary of chemical stability, protein expression of our mRNA designs (A–G) and their immunogenicity in the induction of anti-Spike IgG compared to the codon-optimized baseline (H). b, Non-denaturing agarose gel characterization of mRNA showing the correlation of gel mobility with minimum free energy; for gel source data, see Supplementary Fig. 1a. c, Chemical stability of mRNAs upon incubation in buffer (Mg2+ = 10 mM) at 37 °C. Percentage of intact mRNA is shown. Data is from three independent experiments. d, Protein expression levels of mRNAs determined by flow cytometry 48 hours after transfection into HEK-293 cells. Mean fluorescence intensity (MFI) values derived from three independent experiments are shown. Kruskal–Wallis analysis of variance (ANOVA) with Dunn's multiple comparisons test to H group was performed for statistical analysis. e–g, C57BL/6 mice (n=6) were immunized i.m. with two doses of formulated mRNA at a 2-week interval. Endpoint titer of anti-Spike IgG (e). Levels of neutralizing Abs against wide-type SARS-CoV-2 (f). Frequencies of IFN-γ-secreting T cells measured by ELISpot (g). A two-tailed Mann-Whitney U test was used for statistical analysis. *p < 0.05, **p < 0.01, ***p < 0.001. Data are presented as mean ± s.d. (c, d), geometric mean ± geometric s.d. (e, f) or mean ± s.e.m. (g). See Source Data for details.. Credit: Baidu Research 新的人工智能算法提高COVID-19 mRNA疫苗的抗体反应128倍的](https://scx1.b-cdn.net/csz/news/800a/2023/new-ai-algorithm-boost-1.jpg)
研究人员使用技术语言处理称为晶格解析,代表潜在的词连接在一个点阵图并选择最合理的选择基于语法。同样,他们创建了一个简洁的图形代表所有mRNA候选人,使用确定性有限状态自动机(DFA)。晶格解析应用到mRNA,找到最佳的mRNA类似于确定最可能的句子中一系列像模像样的替代品。
使用这种方法,LinearDesign仅11分钟生成最稳定的信使rna序列编码蛋白质。
设计的详细比较,序列LinearDesign序列表现出明显改善的结果相比,现有的疫苗。COVID-19 mRNA疫苗序列,该算法实现了稳定增加5倍(mRNA半衰期),增加三倍的蛋白质表达水平(在48小时内),和一个令人难以置信的128倍增加抗体反应。对于带状疱疹疫苗信使rna序列,研究报道稳定到6倍增长(mRNA分子半衰期),增加了5.3倍(48小时)蛋白表达水平,增加抗体反应和一个8倍。
“疫苗设计通过我们的方法具有相同剂量可能提供更好的保护,并有可能为平等保护提供一个更小的剂量,从而减少副作用。这将大大减少生物制药公司的疫苗研究和开发成本,同时改善结果,”Zhang博士补充说。2021年,百度和赛诺菲开始合作将LinearDesign算法集成到赛诺菲的产品设计管道mRNA疫苗和药物的开发。
百度已经创建了一个名为PaddleHelix的基于PaddlePaddle bio-computing平台,包括ERNIE-Bio-Computing大模型。这个平台探索人工智能的应用在各个领域,如小分子、蛋白质/肽和RNA,提供一种新型人工智能在生命科学的研究范式。百度的厄尼大模型开发了一个综合模型技术系统,覆盖NLP,视野,跨通道,bio-computing。最近公布了欧尼机器人,丰富知识大语言模型(LLM)能够理解人类语言和生成,是家庭厄尼大模型的一部分。
前进,百度将继续探索人工智能应用于生命科学、拓宽包容的范围和深度技术,倡导全人类的健康和福祉。
更多信息:他张et al,算法优化mRNA设计提高了稳定性和免疫原性,自然(2023)。DOI: 10.1038 / s41586 - 023 - 06127 - z