Graduate Group in Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
Nat Struct Mol Biol. 2018 Jul;25(7):577-582. doi: 10.1038/s41594-018-0080-2. Epub 2018 Jul 2.
Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output.
同义密码子的选择会对核糖体的速度和蛋白质的表达产生显著的影响。核糖体分析实验强调了核糖体在 mRNA 上的移动并不是匀速的。在这里，我们使用前馈神经网络来模拟翻译延伸过程中的这种变化，将核糖体密度作为其序列邻域的函数进行预测。我们的方法揭示了影响翻译延伸的序列特征，并描述了核糖体分析中的大型技术偏差。我们将我们的模型应用于设计跨越模型预测的翻译速度范围的荧光蛋白的同义变体。在出芽酵母中，荧光蛋白的水平与预测的翻译速度在其整个范围内密切相关。因此，我们证明了我们的模型可以捕捉到决定体内翻译动态的信息；可以利用这些信息来设计编码序列；并且单独控制翻译延伸就足以在蛋白质输出中产生大的定量差异。
