Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data.
Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data.
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DOI：
10.1371/journal.pcbi.1004336
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发表时间：
2015-08
影响因子：
4.3
通讯作者：
de Ridder D
中科院分区：
文献类型：
--
作者：
Gritsenko AA;Hulsman M;Reinders MJ;de Ridder D
将RNA翻译成蛋白质是任何生物体的核心过程。虽然这一过程的某些步骤对蛋白质生产的影响是了解的，但对翻译的整体理解仍然难以捉摸。计算机模拟是阐明蛋白质合成过程的一种很有前途的方法。虽然已经提出了一些计算模型的过程中，他们的应用是有限的假设。核糖体分析（RP），一个相对较新的测序为基础的技术，能够记录快照的积极翻译核糖体的位置，是一个很有前途的信息来源，推导无偏的数据驱动的翻译模型。然而，RP数据的定量分析是具有挑战性的，由于高测量方差和无法区分在基因上测量的核糖体的数量和它们的翻译速度。我们提出了一个解决方案的形式，一个新的多尺度的RP数据的解释，允许从快照中提取的翻译动态模型。我们证明了这种方法的有用性，同时确定第一次每个密码子翻译延伸和每个基因的翻译起始率的酿酒酵母RP数据的两个版本的完全不对称排除过程（TASEP）模型的翻译。我们这样做是在一个公正的方式，通过拟合模型，只使用RP数据与一个新的优化方案的基础上蒙特卡洛模拟，以保持问题易于处理。拟合模型与数据的匹配明显优于现有模型，并且它们的预测与几个独立的蛋白质丰度数据集的一致性优于现有模型。结果还表明，tRNA池适应假说是不完整的，有证据表明，tRNA的转录后修饰和密码子上下文可能在决定密码子延长率的作用。翻译是从mRNA模板合成蛋白质的过程，是所有生物体中必不可少的生物学过程。更好地理解这一过程将在各个领域产生影响，从基因调控、疾病理解和医学到生物技术和合成生物学。尽管如此，对翻译过程的整体理解仍然是难以捉摸的，这使得计算建模成为研究翻译过程的一种有前途的方法。然而，由于这些模型所做的许多假设以及需要指定的参数数量之多，准确的翻译建模是具有挑战性的。在这里，我们建议将翻译模型拟合到核糖体分析测量上，该测量记录了来自数百万个细胞的mRNA上活跃翻译核糖体的位置的快照。我们开发了统计和计算方法，用于拟合完全不对称排除过程（TASEP）模型的翻译这些测量和验证他们推导出高度准确的翻译模型的面包酵母酿酒酵母，这优于现有的模型在独立的数据集。我们发现，拟合的延伸率参数从衍生的模型偏离广泛接受的tRNA池适应假说显着。
Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates. Translation, the process of synthesizing proteins from mRNA templates, is an essential biological process in all living organisms. A better understanding of this process will have ramifications in various fields—from gene regulation, disease understanding and medicine to biotechnology and synthetic biology. Nonetheless, a holistic understanding of the processes remains elusive, making computational modelling a promising approach for studying it. However, accurate modelling of translation is challenging due to many assumptions made by such models and due to the sheer number of parameters that need to be specified. Here, we propose to fit models of translation onto ribosome profiling measurements, which record snapshots of the locations of actively translating ribosomes on mRNAs from millions of cells. We develop statistical and computational methods for fitting the Totally Asymmetric Exclusion Process (TASEP) models of translation on these measurements and verify them by deriving highly accurate translation models for the baker’s yeast Saccharomyces cerevisiae, which outperform existing models on independent datasets. We find that fitted elongation rate parameters from the derived models deviate significantly from the widely accepted tRNA pool adaptation hypothesis.