Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria.
Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria.
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DOI：
10.15252/msb.20134955
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发表时间：
2014-06-21
影响因子：
9.9
通讯作者：
Salis HM
中科院分区：
文献类型：
--
作者：
Farasat I;Kushwaha M;Collens J;Easterbrook M;Guido M;Salis HM
开发多蛋白质遗传系统的预测模型以理解和优化其行为仍然是一个组合挑战，特别是当测量通量有限时。我们开发了一种计算方法来构建预测模型并确定最佳序列和表达水平，同时避免组合爆炸。最大信息量的遗传系统变体首先由RBS文库计算器设计，该算法设计序列，用于使用定制的搜索参数和良好预测的翻译速率在> 10，000倍范围内有效搜索多蛋白质表达空间。我们通过表征646个遗传系统变体来验证该算法的预测，这些变体编码在质粒和基因组中，在6种革兰氏阳性和革兰氏阴性细菌宿主中表达。然后，我们将搜索算法与系统级动力学建模相结合，需要构建和表征73个变体，以构建生物合成途径的序列-表达-活性图谱（SEAMAP）。使用模型预测，我们设计并表征了47个额外的途径变体，以导航其活性空间，找到具有所需活性响应曲线的最佳表达区域，并减轻代谢中的限速步骤。创建序列-表达-活性图谱加速了许多蛋白质系统的优化，并允许以前的测量为未来的设计提供定量信息。
Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.