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Systems optimisation of host cell tRNA usage and codon decoding for the improvement of bioprocessing parameters

宿主细胞 tRNA 使用和密码子解码的系统优化，以改善生物加工参数

基本信息

批准号：
BB/I010351/1
负责人：
Tobias Von Der Haar
金额：
$ 8.47万
依托单位：
University of Kent
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FI010351%2F1
关键词：
Systems optimisation host cell tRNA

项目摘要

The decoding of genes during protein synthesis is a complex process that must occur with great accuracy in order for cells and organisms to remain healthy. Accurate protein synthesis is achieved through the interplay of many different molecules, including ribosomes (the molecular machines that actually achieve protein synthesis), tRNAs (adapter molecules that transport amino acids to the ribosome), and translation factors (helper proteins that establish the correct contact between ribosomes and tRNAs). In order to achieve accurate protein synthesis it is critical that the levels of each of these elements are matched exactly to the frequency with which they are used: if cells contain too much or too little of any of these elements, protein synthesis errors occur more frequently and cellular health declines. In normal cells that only produce proteins from their own genes, the protein synthesis system and levels of the molecules described above are optimised to achieve the required low error rates and high translational speed. However, in industrial applications additional genes are often introduced into cells with the aim of producing specific proteins that are not naturally produced by them. This strategy is used in hte pharmaceutical industry to produce the latest generation drugs against cancer, multiple sclerosis and arthritis. When cells make proteins from foreign or artificial genes, the protein synthesis machinery must deal with a situation for which it has not been optimised. We predict that this will increase error rates during the production of the relevant proteins. Protein synthesis errors have negative effects for the ease with which protein-based drugs can be purified and formulated following synthesis in the host cells, and may also adversely affect the potency of the final product. A second prediction we make is that, if we understood the principles of optimisation in detail, we might develop strategies that restore optimal protein synthesis and reduce error rates. Both predictions follow logically from existing knowledge of the translational machinery, although to date they have not yet been experimetnally tested and therefore we can not be completely sure whether they are true. Because our predictions on the relationship between optimised protein synthesis and expression of foreign proteins have important consequences for our ability to make high-quality protein-based drugs, we wish to test them in a small pilot study. We will develop computational models of protein synthesis that will help us to understand the principles of optimisation in protein synthesis. We will then use thes models to suggest strategies for achieving optimisation under conditions of foreign protein synthesis in a simple yeast-based expression system. Lastly, we will test experimentally whether these strategies do indeed improve the quality of proteins, and facilitate their processing following synthesis. If this pilot study confirms our predictions, we will use this as basis for a larger study in which we develop optimisation strategies for the various protein synthesis systems used in the pharmaceutical industry.

蛋白质合成期间的基因解码是一个复杂的过程，必须非常准确地进行，才能使细胞和生物体保持健康。精确的蛋白质合成是通过许多不同分子的相互作用实现的，包括核糖体（实际实现蛋白质合成的分子机器），tRNA（将氨基酸转运到核糖体的衔接分子）和翻译因子（在核糖体和tRNA之间建立正确接触的辅助蛋白）。为了实现准确的蛋白质合成，这些元素中的每一种的水平与它们使用的频率完全匹配是至关重要的：如果细胞含有过多或过少的这些元素，蛋白质合成错误会更频繁地发生，细胞健康状况会下降。在仅从自身基因产生蛋白质的正常细胞中，蛋白质合成系统和上述分子的水平被优化以实现所需的低错误率和高翻译速度。然而，在工业应用中，通常将额外的基因引入细胞中，目的是产生非天然产生的特定蛋白质。这一策略被用于制药业，生产最新一代的抗癌、多发性硬化和关节炎药物。当细胞从外源基因或人工基因中合成蛋白质时，蛋白质合成机制必须应对尚未优化的情况。我们预测，这将增加相关蛋白质生产过程中的错误率。蛋白质合成错误对基于蛋白质的药物在宿主细胞中合成后可被纯化和配制的容易性具有负面影响，并且还可能不利地影响最终产物的效力。我们的第二个预测是，如果我们详细了解优化原理，我们可能会开发出恢复最佳蛋白质合成并降低错误率的策略。这两个预言都是从翻译机制的现有知识中逻辑地得出的，尽管到目前为止它们还没有经过实验的检验，因此我们不能完全确定它们是否正确。由于我们对优化的蛋白质合成和外源蛋白质表达之间关系的预测对我们制造高质量蛋白质药物的能力具有重要影响，因此我们希望在小型试点研究中对其进行测试。我们将开发蛋白质合成的计算模型，这将有助于我们理解蛋白质合成的优化原则。然后，我们将使用这些模型来建议策略，以实现在一个简单的基于酵母的表达系统中的外源蛋白质合成条件下的优化。最后，我们将通过实验测试这些策略是否确实提高了蛋白质的质量，并促进其在合成后的加工。如果这项初步研究证实了我们的预测，我们将以此为基础进行更大规模的研究，为制药行业中使用的各种蛋白质合成系统开发优化策略。