Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources

使用 CHO 细胞基因组资源将重组基因序列与蛋白质产品可制造性联系起来

• 批准号: BB/K011197/1
• 负责人: David James
• 金额: $ 83.45万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK011197%2F1
• 关键词: Linking; recombinant; gene; sequence; protein

Biopharmaceutical companies producing the new generation of recombinant DNA derived therapeutic proteins (e.g. cancer medicines such as Herceptin and Avastin) often use mammalian cells grown in culture to make the protein product. All production processes are based, fundamentally, upon the ability of the host mammalian cell factory to use a synthetic DNA genetic "code" to manufacture the complex protein product. This is a cornerstone of modern biotechnology. However, because protein synthesis is so complex, involving many cellular resources and machines, it is extremely difficult for genetic engineers to design a DNA code that will best enable the mammalian cell factory to operate most efficiently. Moreover, as individual mammalian cell factories can be very variable, they may differ substantially in their relative ability to make the product. As a consequence, a lot of time and money has to be spent by companies on the initial phases of the biopharmaceutical development process conducting intensive screening operations to find the best cell factory (out of a large population) able to use the genetic code it has been given. For a different protein product it is necessary to start the whole development process again.In this project we will utilise recently available high information content molecular analysis technologies and computational tools to "de-convolute" the complexity of protein synthesis in mammalian cell factories. Effectively, we know that the mammalian cell factory uses its own genetic code to make thousands of its own proteins (machines) that together perform a variety of functions that enable the cell to grow and divide. The rate at which these proteins are made varies hugely, over 1000-fold, so that the cell can make each bit of protein machinery in the right quantity to do its job. We will measure how efficiently each cellular protein is made then using advanced biological information analysis (bioinformatics) and mathematics we will determine how the cell uses pieces of information embedded in each of its genes to vary the rate at which a specific protein is made.This will enable us to create, for the first time, a usable set of "design rules" (computer programmes) that genetic engineers and cell factory developers can employ to (i) reliably design the best genetic code for any given protein product and (ii) accurately predict how much of the protein product the mammalian cell factory can make. This is important as it means that biopharmaceutical companies can design a predictable production system from scratch, enabling a more rapid transition through lengthy cell factory development processes towards (pre-)clinical trials.

生产新一代重组DNA衍生治疗蛋白(例如，赫赛汀和阿瓦斯丁等抗癌药物)的生物制药公司经常使用培养中的哺乳动物细胞来制造蛋白质产品。从根本上说，所有的生产过程都是基于宿主哺乳动物细胞工厂使用合成DNA遗传“密码”来制造复杂蛋白质产品的能力。这是现代生物技术的基石。然而，由于蛋白质合成如此复杂，涉及许多细胞资源和机器，基因工程师极难设计出一种DNA代码，使哺乳动物细胞工厂能够最有效地运行。此外，由于个别哺乳动物细胞工厂的差异很大，它们生产产品的相对能力可能会有很大差异。因此，公司不得不在生物制药开发过程的初始阶段花费大量时间和金钱，进行密集的筛选操作，以(从大量人口中)找到能够使用其获得的遗传密码的最佳细胞工厂。对于不同的蛋白质产品，有必要重新开始整个开发过程。在这个项目中，我们将利用最近可用的高信息量分子分析技术和计算工具来解开哺乳动物细胞工厂中蛋白质合成的复杂性。实际上，我们知道哺乳动物细胞工厂使用自己的遗传密码来制造数千种自己的蛋白质(机器)，这些蛋白质(机器)一起执行各种功能，使细胞能够生长和分裂。这些蛋白质的合成速度差异很大，超过1000倍，因此细胞可以制造出每一块合适数量的蛋白质机器来完成自己的工作。我们将测量每种细胞蛋白质的制造效率，然后使用先进的生物信息分析(生物信息学)和数学方法，我们将确定细胞如何使用嵌入在其每个基因中的信息片段来改变特定蛋白质的制造速度。这将使我们第一次能够创建一套可用的“设计规则”(计算机程序)，基因工程师和细胞工厂开发人员可以使用它来(I)可靠地为任何给定的蛋白质产品设计最佳遗传密码，(Ii)准确地预测哺乳动物细胞工厂可以生产多少蛋白质产品。这一点很重要，因为这意味着生物制药公司可以从头开始设计一种可预测的生产系统，通过漫长的细胞工厂开发过程更快地过渡到(临床前)试验。

项目成果

Variational Auto-encoded Deep Gaussian Processes

• 发表时间: 2015-11
• 期刊: arXiv: Learning
• 作者: Zhenwen Dai;Andreas C. Damianou;Javier I. González;Neil D. Lawrence

In silico design of context-responsive mammalian promoters with user-defined functionality.

• DOI: 10.1093/nar/gkx768
• 发表时间: 2017-10-13
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Brown AJ;Gibson SJ;Hatton D;James DC
• 通讯作者: James DC

Bayesian Optimization for Synthetic Gene Design

合成基因设计的贝叶斯优化

• DOI: 10.48550/arxiv.1505.01627
• 发表时间: 2015
• 期刊: arXiv e-prints
• 作者: Gonz