Deducing the kinetics of protein synthesis in vivo from the transition rates measured in vitro.
Deducing the kinetics of protein synthesis in vivo from the transition rates measured in vitro.
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DOI：
10.1371/journal.pcbi.1003909
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发表时间：
2014-10
影响因子：
4.3
通讯作者：
Lipowsky R
中科院分区：
文献类型：
--
作者：
Rudorf S;Thommen M;Rodnina MV;Lipowsky R
生命的分子机制依赖于复杂的多步骤过程，其中涉及许多单独的转变，如分子缔合和解离步骤，化学反应和机械运动。相应的转换速率通常可以在体外测量，但不能在体内测量。在这里，我们开发了一种通用的方法来推导出体内率从他们的体外值。该方法有两个基本组成部分。首先，我们引入了动力学距离，一个新的概念，我们可以定量地比较在不同环境中的多步过程的动力学。动力学距离依赖于转换率，可以解释为潜在的自由能障碍。其次，我们最大限度地减少在体外和体内过程之间的动力学距离，施加的约束，推导出的速率再现一个已知的全局属性，如整体在体内的速度。为了证明我们的方法的预测能力，我们将其应用于蛋白质合成的核糖体，基因表达的关键过程。我们描述了后一个过程的密码子特定的马尔可夫模型与三个反应途径，对应于同源，近同源，和非同源tRNA的初始结合，我们确定所有个人的转换率在体外。然后，我们预测体内率的约束最小化程序和验证这些利率由三个独立的体内数据集，获得密码子依赖的翻译速度，密码子特定的翻译动力学，和错义错误频率。在所有情况下，我们发现理论和实验之间的良好协议，而无需调整任何拟合参数。推导出的体内速率导致比已知的体外速率更小的错误频率，主要是通过改进的tRNA的初始选择。这里介绍的方法是相对简单的，从计算的角度来看，可以应用于任何生物分子的过程中，我们有详细的信息，在体外动力学。“生命即运动”这句谚语也适用于分子尺度。事实上，如果我们以分子分辨率观察任何活细胞，我们将观察到大量各种高度动态的过程。这些动力学的一个特别引人注目的方面是，细胞内的所有大分子都是通过复杂的生物分子机器连续合成、修饰和降解的。这些“纳米机器人”遵循复杂的反应途径，形成分子转换或转化步骤的网络。这些步骤中的每一个都是随机的，平均需要一定的时间。一个根本性的重要问题是，这些单独的步骤时间或相应的转换速率如何决定细胞中过程的总体速度。然而，这个问题很难回答，因为步进时间只能在体外测量，而不能在体内测量。在这里，我们开发了一个通用的计算方法，通过该方法可以推导出在体内的个人步骤时间从他们的体外值。为了证明我们的方法的预测能力，我们将其应用于蛋白质合成的核糖体，基因表达的关键过程，并验证推导的步骤时间由三个独立的体内数据集。
The molecular machinery of life relies on complex multistep processes that involve numerous individual transitions, such as molecular association and dissociation steps, chemical reactions, and mechanical movements. The corresponding transition rates can be typically measured in vitro but not in vivo. Here, we develop a general method to deduce the in-vivo rates from their in-vitro values. The method has two basic components. First, we introduce the kinetic distance, a new concept by which we can quantitatively compare the kinetics of a multistep process in different environments. The kinetic distance depends logarithmically on the transition rates and can be interpreted in terms of the underlying free energy barriers. Second, we minimize the kinetic distance between the in-vitro and the in-vivo process, imposing the constraint that the deduced rates reproduce a known global property such as the overall in-vivo speed. In order to demonstrate the predictive power of our method, we apply it to protein synthesis by ribosomes, a key process of gene expression. We describe the latter process by a codon-specific Markov model with three reaction pathways, corresponding to the initial binding of cognate, near-cognate, and non-cognate tRNA, for which we determine all individual transition rates in vitro. We then predict the in-vivo rates by the constrained minimization procedure and validate these rates by three independent sets of in-vivo data, obtained for codon-dependent translation speeds, codon-specific translation dynamics, and missense error frequencies. In all cases, we find good agreement between theory and experiment without adjusting any fit parameter. The deduced in-vivo rates lead to smaller error frequencies than the known in-vitro rates, primarily by an improved initial selection of tRNA. The method introduced here is relatively simple from a computational point of view and can be applied to any biomolecular process, for which we have detailed information about the in-vitro kinetics. The proverb ‘life is motion’ also applies to the molecular scale. Indeed, if we looked into any living cell with molecular resolution, we would observe a large variety of highly dynamic processes. One particularly striking aspect of these dynamics is that all macromolecules within the cell are continuously synthesized, modified, and degraded by complex biomolecular machines. These ‘nanorobots’ follow intricate reaction pathways that form networks of molecular transitions or transformation steps. Each of these steps is stochastic and takes, on average, a certain amount of time. A fundamentally important question is how these individual step times or the corresponding transition rates determine the overall speed of the process in the cell. This question is difficult to answer, however, because the step times can only be measured in vitro but not in vivo. Here, we develop a general computational method by which one can deduce the individual step times in vivo from their in-vitro values. In order to demonstrate the predictive power of our method, we apply it to protein synthesis by ribosomes, a key process of gene expression, and validate the deduced step times by three independent sets of in-vivo data.
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