Department of Microbiology and Physiological Systems, UMass Chan Medical School, 368 Plantation Street, Worcester, MA, 01655, USA.
Department of Genomics and Computational Biology, UMass Chan Medical School, 368 Plantation Street, Worcester, MA, 01655, USA.
Nat Commun. 2024 Mar 20;15(1):2486. doi: 10.1038/s41467-024-46703-z.
Protein synthesis terminates when a stop codon enters the ribosome's A-site. Although termination is efficient, stop codon readthrough can occur when a near-cognate tRNA outcompetes release factors during decoding. Seeking to understand readthrough regulation we used a machine learning approach to analyze readthrough efficiency data from published HEK293T ribosome profiling experiments and compared it to comparable yeast experiments. We obtained evidence for the conservation of identities of the stop codon, its context, and 3'-UTR length (when termination is compromised), but not the P-site codon, suggesting a P-site tRNA role in readthrough regulation. Models trained on data from cells treated with the readthrough-promoting drug, G418, accurately predicted readthrough of premature termination codons arising from CFTR nonsense alleles that cause cystic fibrosis. This predictive ability has the potential to aid development of nonsense suppression therapies by predicting a patient's likelihood of improvement in response to drugs given their nonsense mutation sequence context.
当终止密码子进入核糖体的 A 位时，蛋白质合成就会终止。尽管终止效率很高，但当接近同功的 tRNA 在解码过程中与释放因子竞争时，终止密码子通读仍会发生。为了了解通读调控，我们使用机器学习方法分析了已发表的 HEK293T 核糖体分析实验中的通读效率数据，并将其与可比的酵母实验进行了比较。我们获得了终止密码子及其上下文和 3'-UTR 长度（在终止受到影响时）的保守性的证据，但 P 位密码子没有保守性，这表明 P 位 tRNA 在通读调控中起作用。使用经通读促进药物 G418 处理的细胞数据训练的模型，准确预测了导致囊性纤维化的 CFTR 无义等位基因引起的过早终止密码子的通读。这种预测能力有可能通过预测患者对药物反应的改善可能性来辅助无义抑制疗法的开发，其依据是患者的无义突变序列上下文。
