Intelligent host engineering for metabolic flux optimisation in biotechnology.
Intelligent host engineering for metabolic flux optimisation in biotechnology.
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DOI：
10.1042/bcj20210535
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发表时间：
2021-10-29
期刊：
影响因子：
--
通讯作者：
Kell DB
中科院分区：
文献类型：
--
作者：
Munro LJ;Kell DB
通过定向进化优化长度为N个氨基酸的蛋白质的功能涉及导航大约20 N的可能序列的“搜索空间”。优化P蛋白的表达水平，实质上影响主机的性能，其中每个也可能采取20个（空间间隔）的值，意味着一个类似的搜索空间20 P。因此，在这种组合意义上，定向蛋白质进化和宿主工程的问题大体上是等价的。然而，在实践中，它们有不同的手段来避免执行中不可避免的困难。代谢网络中表现出的备用容量意味着宿主工程可能会大幅增加感兴趣目标的通量。因此，我们排练的相关问题，为那些希望了解和利用这些现代全基因组宿主工程工具和思想，已被设计和开发，以优化生物技术过程中所需的产品通量，重点是微生物系统。目标通量是“使这种生物学可预测”。策略已经针对转录和翻译，特别是对于可以影响多个靶标的调节过程。然而，由于细胞可以产生多少蛋白质是有限制的，因此在选定的靶点中增加kcat可能是比增加蛋白质表达水平更好的策略，以实现最佳宿主工程。
Optimising the function of a protein of length N amino acids by directed evolution involves navigating a ‘search space’ of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is ‘making such biology predictable’. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.
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影响因子：
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作者：
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3.4
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影响因子：
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通讯作者：
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