codABLE: Data-driven optimised protein production using Pichia pastoris

codABLE：使用毕赤酵母进行数据驱动的优化蛋白质生产

• 批准号: 10108143
• 金额: $ 12.73万
• 依托单位: INGENZA LIMITED
• 依托单位国家: 英国
• 项目类别: Launchpad
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10108143
• 关键词: codABLE; Data; driven; optimised; protein

Our project addresses the critical need for viable, sustainable biomanufacturing processes and accelerated development of protein therapeutics, emphasising the importance of predictability when designing DNA sequences to produce valuable heterologous proteins efficiently, using recombinant organisms. Leveraging insights from natural codon usage profiles, we developed codABLE, a machine learning-based platform to customise genetic codon composition to be most compatible with the production host. CodABLE outperforms codon optimisation algorithms operated by commercial DNA synthesis providers and offers a unique advantage in controlling protein expression without altering regulatory regions such as promoters or ribosome binding sequences.Initially applied to _Bacillus subtilis_, codABLE demonstrated repeated success in predicting and enhancing protein expression. Now, we aim to extend this algorithm to _Pichia pastoris_, a more complex eukaryotic organism with particular advantages for use in protein biomanufacturing.Expression data from a large and diverse library of gene variants will be collected by fluorescence activated cell sorting (FACS) and Next Generation Sequencing, to establish a genotype-phenotype relationship. These valuable data will be fed into our machine learning platform, incorporating algorithms like Support Vector Machine and Random Forest, to discern key relationships between codon usage and protein expression.The best-performing algorithm will be used to design DNA sequences to express protein targets of commercial value, serving as both model validation and a compelling solution for those seeking innovative protein expression strategies. Our approach combines cutting-edge computational technology with Ingenza's expertise using diverse microbial hosts and ultra high-throughput FACS screening to increase our business competitiveness and contribute to Scotland's bio-based manufacturing innovation.

我们的项目解决了对可行的、可持续的生物制造工艺和蛋白质治疗加速发展的关键需求，强调了在设计DNA序列时可预测性的重要性，利用重组生物高效地生产有价值的异源蛋白质。利用自然密码子使用概况的见解，我们开发了codABLE，这是一个基于机器学习的平台，用于定制遗传密码子组成，使其与生产宿主最兼容。CodABLE优于商业DNA合成供应商操作的密码子优化算法，在不改变启动子或核糖体结合序列等调控区域的情况下控制蛋白质表达方面具有独特的优势。coable最初应用于枯草芽孢杆菌，多次成功预测和增强蛋白表达。现在，我们的目标是将这种算法扩展到毕氏酵母，一种更复杂的真核生物，在蛋白质生物制造中具有特殊的优势。通过荧光活化细胞分选（FACS）和下一代测序技术，从大量多样的基因变异文库中收集表达数据，建立基因型-表型关系。这些有价值的数据将被输入我们的机器学习平台，结合支持向量机和随机森林等算法，以识别密码子使用和蛋白质表达之间的关键关系。性能最好的算法将用于设计DNA序列来表达具有商业价值的蛋白质目标，作为模型验证和寻求创新蛋白质表达策略的令人信服的解决方案。我们的方法将尖端的计算技术与Ingenza的专业知识结合起来，使用多种微生物宿主和超高通量FACS筛选，以提高我们的业务竞争力，并为苏格兰的生物基制造创新做出贡献。