Rudorf Sophia, Thommen Michael, Rodnina Marina V, Lipowsky Reinhard
Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, Potsdam, Germany.
Physical Biochemistry, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
PLoS Comput Biol. 2014 Oct 30;10(10):e1003909. doi: 10.1371/journal.pcbi.1003909. eCollection 2014 Oct.
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.
生命的分子机制依赖于复杂的多步过程,这些过程涉及众多单独的转变,如分子缔合和解离步骤、化学反应以及机械运动。相应的转变速率通常可以在体外测量,但无法在体内测量。在此,我们开发了一种从体外值推导体内速率的通用方法。该方法有两个基本组成部分。首先,我们引入了动力学距离,这是一个新概念,通过它我们可以定量比较不同环境中多步过程的动力学。动力学距离对数依赖于转变速率,并且可以根据潜在的自由能垒来解释。其次,我们使体外和体内过程之间的动力学距离最小化,施加这样的约束:推导得到的速率要重现一个已知的全局性质,如整体体内速度。为了证明我们方法的预测能力,我们将其应用于核糖体的蛋白质合成,这是基因表达的一个关键过程。我们用一个具有三条反应途径的密码子特异性马尔可夫模型来描述后一过程,这三条途径分别对应同源、近同源和非同源tRNA的初始结合,我们在体外确定了所有单独的转变速率。然后,我们通过约束最小化程序预测体内速率,并通过三组独立的体内数据验证这些速率,这些数据分别是关于密码子依赖性翻译速度、密码子特异性翻译动力学和错义错误频率的。在所有情况下,我们发现理论与实验之间有很好的一致性,而无需调整任何拟合参数。推导得到的体内速率导致的错误频率比已知的体外速率更小,主要是通过改进tRNA的初始选择。从计算角度来看,这里介绍的方法相对简单,并且可以应用于任何我们有体外动力学详细信息的生物分子过程。