The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization.
The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization.
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DOI：
10.1371/journal.pcbi.1005167
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发表时间：
2016-11
影响因子：
4.3
通讯作者：
Liebermeister W
中科院分区：
文献类型：
--
作者：
Noor E;Flamholz A;Bar-Even A;Davidi D;Milo R;Liebermeister W
细菌的生长关键取决于代谢通量，这是由细胞的能力，以维持代谢酶的限制。每单位流量所需的酶量是进化和生物工程中代谢策略的主要决定因素。它取决于酶参数（如kcat和KM常数），但也取决于代谢物浓度。此外，相似数量的不同酶可能会导致细胞产生不同的成本，这取决于酶的特定特性，如蛋白质大小和半衰期。在这里，我们开发了酶成本最小化（ECM），一种可扩展的方法，用于计算以最小蛋白质成本支持给定代谢通量的酶量。酶和代谢物浓度的复杂相互作用，例如通过热力学驱动力和酶饱和度，将使得难以直接解决该优化问题。通过将酶成本作为代谢物水平的函数来处理，我们将ECM制定为数值上易于处理的凸优化问题。它的分层方法允许根据可用数据的数量在不同的详细程度上构建模型。用E.大肠杆菌中心代谢，我们发现典型的预测倍数误差分别为4.1和2.6，为两种数据。这一结果从成本优化的代谢状态是显着优于随机采样的代谢产物谱，支持的假设，酶的成本是重要的健身E。杆菌ECM可用于预测天然和工程途径中的酶水平和蛋白质成本，并且可能是辅助代谢工程项目的有价值的计算工具。此外，它建立了蛋白质成本和热力学之间的直接联系，并提供了一个物理上合理和计算上易于处理的方式，将酶动力学纳入基于约束的代谢模型，其中动力学通常被忽略或过度简化。“酶成本”，即给定代谢通量所需的蛋白质量，对于细胞必须做出的代谢选择至关重要。然而，由于线性优化方法的技术限制，传统上基于约束的代谢模型（如通量平衡分析）忽略了这种成本。另一方面，更详细的动力学模型，使用常微分方程来模拟不同选择的酶分配的通量，计算要求很高，可扩展性不够。在这项工作中，我们开发了一种方法，利用全动力学模型来预测稳态酶的成本，使用一个可扩展的和强大的算法的基础上凸优化。我们表明，酶成本最小化是一个有意义的最优性原则，通过比较我们的预测，以测得的酶和代谢物水平在指数增长的E。杆菌这种方法可以用来量化许多其他途径的酶成本，并解释为什么进化选择了一些低产量的代谢策略，包括酵母和癌细胞中的有氧发酵。此外，未来的代谢工程项目可以从我们的方法中受益，通过选择减少合成增值产品所需的酶总量的途径。
Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified. “Enzyme cost”, the amount of protein needed for a given metabolic flux, is crucial for the metabolic choices cells have to make. However, due to the technical limitations of linear optimization methods, this cost has traditionally been ignored by constraint-based metabolic models such as Flux Balance Analysis. On the other hand, more detailed kinetic models which use ordinary differential equations to simulate fluxes for different choices of enzyme allocation, are computationally demanding and not scalable enough. In this work, we developed a method which utilizes the full kinetic model to predict steady-state enzyme costs, using a scalable and robust algorithm based on convex optimization. We show that the minimization of enzyme cost is a meaningful optimality principle by comparing our predictions to measured enzyme and metabolite levels in exponentially growing E. coli. This method could be used to quantify the enzyme cost of many other pathways and explain why evolution has selected some low-yield metabolic strategies, including aerobic fermentation in yeast and cancer cells. Furthermore, future metabolic engineering projects could benefit from our method by choosing pathways that reduce the total amount of enzyme required for the synthesis of a value-added product.
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