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，可持续的生物制造过程的关键需求，并加速了蛋白质疗法的发展，并强调了使用重组生物在设计DNA序列以有效地产生有价值的异源蛋白质时可预测性的重要性。利用天然密码子使用概况的见解，我们开发了Codable，这是一种基于机器学习的平台，以自定义遗传密码子组成，以与生产宿主最兼容。代码优于商业DNA合成提供商操作的密码子优化算法，并在控制蛋白表达方面具有独特的优势，而无需改变调节区域（例如启动子或核糖体结合序列）。在_bacillus uctilis _，编码中，编码在预测和增强蛋白质表达方面反复出现。现在，我们的目标是将此算法扩展到_pichia pastoris_，这是一种更复杂的真核生物生物，具有蛋白质生物制造的特殊优势。来自荧光激活细胞分类（Faces）和下一代测序的大型基因变异库中的表达数据将收集，以建立一种遗传类型 - 明显相关。这些有价值的数据将被馈入我们的机器学习平台，结合了诸如支持矢量机和随机森林之类的算法，以辨别密码子使用和蛋白质表达之间的关键关系。最佳表现算法将用于设计DNA序列，以表达商业价值的蛋白质靶标，既可以用作商业价值的蛋白质目标，又可以用作模型验证的解决方案，并寻求那些寻求蛋白质表达的蛋白质表达策略。我们的方法将最先进的计算技术与Ingenza的专业知识结合在一起，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基于生物的制造创新做出贡献。