Harmony AI: State of the Art Natural Language Processing for Genetic Engineering

项目摘要/摘要： 基因工程的计算技术，如密码子优化，使用同义词 密码子的改变会增加蛋白质的产量。这些计算基因的应用 优化措施包括重组蛋白质药物、核酸疗法和基因疫苗。 尽管密码子优化增加了某些系统中的蛋白质产量，但同义词 基因序列的改变可能会对蛋白质造成意想不到的有害结果。此外， 研究人员一直批评将密码子优化作为人类治疗的优化 加工过程会影响蛋白质的构象和功能，降低功效。因此，密码子 优化可能不会为增加蛋白质产量或设计提供最佳策略 安全有效的疗法。中心利用了最先进的自然语言处理技术 学习目标生物体如何使用同义密码子并应用这种学习的技术 为了基因工程。我们证明了我们的模型可以预测E.Coli的同义密码子 使用准确率为73%，大大高于之前的报告。我们相信，使用这种基于人工智能的 基因工程的方法将为提高蛋白质产量提供最佳策略 并可能增加治疗的疗效。

Project Summary/Abstract: Computational techniques for gene engineering, such as codon optimization, use synonymous codon changes to increase protein production. Applications for these computational gene optimizations include recombinant protein drugs, nucleic acid therapies, and mRNA vaccines. Although codon optimization increases protein production in certain systems, synonymous changes to a gene sequence can cause unexpected detrimental results to the protein. Further, researchers have been critical of codon optimization for human therapeutics as the optimization process can affect protein conformation and function, and reduce efficacy. Therefore, codon optimization may not provide an optimal strategy for increasing protein production or designing safe and effective therapeutics. CFDRC has utilized state-of-the-art natural language processing techniques to learn how synonymous codons are used by a target organism and apply this learning to gene engineering. We demonstrated our model could predict the E. Coli synonymous codon usage with 73% accuracy, significantly above prior reports. We believe that using this AI-based approach to gene engineering will provide an optimal strategy for increasing protein production and may increase the efficacy of therapeutics.