近日，美国华盛顿大学的Georg Seelig研究组，利用大规模的平行翻译分析预测了人类mRNA上的5’端非翻译序列对蛋白质翻译的影响。相关结果发表在了2019年7月年出版的《自然—生物技术》上。

研究人员利用了一个包含28万条随机5’端非翻译序列（5’UTR）的库进行多核糖体分析，并联合深度学习来建立了人5’UTR序列与翻译的预测模型。结合遗传学算法，研究人员使用这个模型设计了新的5’UTR能够准确地指导特定的核糖体装载，从而使得优化序列来改变蛋白表达水平变为可能。这个方法能进一步应用至化学修饰的RNA，这对于mRNA治疗和合成生物学方面的应用而言很重要。研究人员人员测试了35,212条截短的人5'UTR和3,577条自然出现的突变，并发现该模型能够预测这些序列中核糖体的装载情况。最后，研究人员建立了45种单核苷酸突变影响核糖体装载与人类疾病之间的关联，因此这可能为疾病的发生提供分子基础。

据了解，预测顺式调控序列在基因表达的影响能够促进基础生物学和应用生物学领域相关的发现。

附：英文原文

Title: Human 5′ UTR design and variant effect prediction from a massively parallel translation assay

Author: Paul J. Sample, Ban Wang, David W. Reid, Vlad Presnyak, Iain J. McFadyen, David R. Morris, Georg Seelig

Issue&Volume: 2019-07-01

Abstract: The ability to predict the impact of cis-regulatory sequences on gene expression would facilitate discovery in fundamental and applied biology. Here we combine polysome profiling of a library of 280,000 randomized 5′ untranslated regions (UTRs) with deep learning to build a predictive model that relates human 5′ UTR sequence to translation. Together with a genetic algorithm, we use the model to engineer new 5′ UTRs that accurately direct specified levels of ribosome loading, providing the ability to tune sequences for optimal protein expression. We show that the same approach can be extended to chemically modified RNA, an important feature for applications in mRNA therapeutics and synthetic biology. We test 35,212 truncated human 5′ UTRs and 3,577 naturally occurring variants and show that the model predicts ribosome loading of these sequences. Finally, we provide evidence of 45 single-nucleotide variants (SNVs) associated with human diseases that substantially change ribosome loading and thus may represent a molecular basis for disease.

DOI: 10.1038/s41587-019-0164-5

Source: https://www.nature.com/articles/s41587-019-0164-5