codABLE: Data-driven optimised protein production using Pichia pastoris

我们的项目满足了对可行的、可持续的生物制造过程和蛋白质疗法加速发展的迫切需求，强调了在设计DNA序列以使用重组生物高效生产有价值的异源蛋白质时可预测性的重要性。利用对自然密码子使用情况的洞察，我们开发了一个可编码的、基于机器学习的平台，以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节区的情况下控制蛋白质表达方面具有独特的优势。最初应用于枯草芽孢杆菌，Codable在预测和增强蛋白质表达方面反复成功。现在，我们的目标是将该算法扩展到巴斯德毕赤酵母，这是一种更复杂的真核生物，具有用于蛋白质生物制造的独特优势。通过荧光激活细胞分类(FACS)和下一代测序，将从大量不同的基因变异库中收集表达数据，以建立基因-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。性能最好的算法将被用于设计DNA序列，以表达具有商业价值的蛋白质靶标，作为模型验证和寻求创新蛋白质表达策略的引人注目的解决方案。我们的方法将尖端计算技术与Ingenza的专业知识相结合，使用不同的微生物宿主和超高通量FACS筛查，以提高我们的业务竞争力，并为苏格兰的生物制造业创新做出贡献。

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.