Machine learning driven codon optimisation for heterologous protein expression

广告项目描述（最多500字）。这将是广告给未来的学生的文本：背景。蛋白质的表达需要将DNA转录成RNA，然后将其翻译成氨基酸序列。每个氨基酸都由称为密码子的三联核苷酸编码，密码子在自然界中是通用的。然而，一种氨基酸可以由不同的密码子编码，这种现象称为遗传密码的简并性，并且使用密码子而不是另一个影响下游蛋白质丰度。有趣的是，尽管合成机制在物种间相对保守，但同义密码子的使用在物种间甚至在基因间存在差异，这取决于许多因素，包括GC含量、重组率、mRNA稳定性和密码子位置[Novoa et al，2019]。此外，已经表明，一旦使用给定的密码子，编码相同氨基酸的后续密码子不是随机挑选的，而是遵循复杂的组合模式[Cannarozzi et al，2010]。尽管高通量测序和蛋白质组学实验产生了丰富的知识，但支撑密码子使用的规则大多是未知的。从工业生物技术的角度来看，这种知识差距限制了我们有效表达异源蛋白质和优化最终用户应用特性（如溶解度）的能力[Pellizza et al，2018]。目标和目标。与Fujifilm Diosynth Biotechnologies UK（FDBK）合作，我们建议通过将蛋白质合成重新表述为语言建模问题来学习密码子使用规则。然后，我们将使用深度学习来捕获与高表达基因和最佳溶解度相关的复杂上位性和进化模式。最终，这些模型将通过计算机模拟和体内验证。工作计划。该项目分为三个工作包。- WP 1-学生将收集E.从公共知识库中提取大肠杆菌，并生成策划的转录本和相关蛋白质序列的数据集。- WP 2-学生将开发一个神经语言模型，通过考虑进化信息和蛋白质功能，将氨基酸序列转换为DNA序列。- WP 3-通过在大肠杆菌中合成、构建和表达密码子优化的蛋白质，实验验证模型的有效性。大肠杆菌中进行比较，并与野生型变异体和用现有方法优化的基因进行下游比较。学生将接受机器学习，统计学习和深度学习方面的培训，并将在生物序列建模和设计方面建立有竞争力的形象。学生还将被介绍到合成生物学的新兴领域，并将学习现代DNA克隆和组装技术以及大规模使用蛋白质表达系统。我们还非常重视可重复的研究;学生将接受高级研究软件工程和数据分析可重复工作流程的培训。

Project Description for Advert (max 500 words). This will be the text that is advertised to prospective students:BACKGROUND. Expression of proteins requires the transcription of DNA into RNA, followed by its translation into amino acid sequences. Each amino acid is encoded by triplets of nucleotides, called codons, which are universal in Nature. However, an amino acid can be encoded by different codons, a phenomenon known as degeneracy of the genetic code, and the use of a codon instead of another affects downstream protein abundance. Interestingly, despite the synthesis machinery is relatively conserved across species, synonymous codons usage varies across species and even across genes, as a function of a number of factors, including GC content, recombination rates, mRNA stability and codon position [Novoa et al, 2019]. Moreover, it has been shown that once a given codon is used, subsequent codons encoding the same amino acid are not randomly picked but follow complex combinatorial patterns [Cannarozzi et al, 2010].Despite the wealth of knowledge generated by high-throughput sequencing and proteomics experiments, the rules underpinning codon usage are mostly unknown. From an industrial biotechnology perspective, this knowledge gap limits our ability to efficiently express heterologous proteins and to optimise properties for end-user applications, such as solubility [Pellizza et al, 2018]. AIMS AND OBJECTIVES. In collaboration with Fujifilm Diosynth Biotechnologies UK (FDBK), we propose to learn codon usage rules by rephrasing protein synthesis as a language modelling problem. We will then use deep learning in order to capture complex epistatic and evolutionary patterns associated with highly expressed genes and with optimal solubility. Ultimately, these models will be validated in silico and in-vivo.WORKPLAN. The project is structured in 3 work packages. - WP1 - the student will collect transcriptomic data for E. coli from public repositories and generate a dataset of curated transcripts and associated protein sequences. - WP2 - the student will develop a neural language model to convert amino acid sequences into DNA sequences, by taking into account evolutionary information and protein function. - WP3 - experimental validation of models' effectiveness, by synthesizing, building and expressing codon optimised proteins in E. coli and performing downstream comparison against wild-type variants and genes optimised with existing methods.TRAINING PROGRAM. The student will receive training in machine learning, statistical learning and deep learning, and will build a competitive profile in biological sequence modelling and design. The student will be also introduced to the emerging field of synthetic biology and will learn modern DNA cloning and assembly techniques and the use of protein expression systems at scale. We also put a strong emphasis on reproducible research; the student will receive training in advanced research software engineering and in reproducible workflows for data analyses.