Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093-0412, USA.
Science. 2013 Jun 7;340(6137):1220-3. doi: 10.1126/science.1234012.
Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein thermostability in a network context. This analysis allowed the prediction of protein activities that limit network function at superoptimal temperatures and mechanistic interpretations of mutations found in strains adapted to heat. Predicted growth-limiting factors for thermotolerance were validated through nutrient supplementation experiments and defined metabolic sensitivities to heat stress, providing evidence that metabolic enzyme thermostability is rate-limiting at superoptimal temperatures. Inclusion of structural information expanded the content and predictive capability of genome-scale metabolic networks that enable structural systems biology of metabolism.
基因组尺度网络重建使许多系统的代谢预测建模成为可能。传统上，蛋白质结构信息并未在这些重建中得到体现。通过包括实验和预测的蛋白质结构，对大肠杆菌代谢的基因组尺度模型进行扩展，使我们能够在网络环境中分析蛋白质热稳定性。这种分析可以预测在超最佳温度下限制网络功能的蛋白质活性，并对适应热的菌株中发现的突变进行机制解释。通过营养补充实验验证了耐热性的预测生长限制因素，并定义了对热应激的代谢敏感性，这为代谢酶热稳定性在超最佳温度下是限速因素提供了证据。结构信息的纳入扩展了基因组尺度代谢网络的内容和预测能力，使代谢的结构系统生物学成为可能。
