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Harmony AI: Natural Language Processing Enabling Advanced Biomanufacturing

Harmony AI：自然语言处理实现先进生物制造

基本信息

批准号：
10761082
负责人：
David Gaddes
金额：
$ 22.92万
依托单位：
CFD RESEARCH CORPORATION
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10761082
关键词：
Affect Amino Acid Sequence Antibodies Artificial Intelligence Biological Biological Assay Biomanufacturing Cell Proliferation Cells Code Codon Nucleotides Computational Technique Computer software Custom DNA Sequence Dependence Drug Delivery Systems Engineered Gene Ensure Enzymes Escherichia coli Escherichia coli Proteins Evaluation Food Food production Frequencies Genes Goals Growth Head Immunosorbents Learning Link Literature Measures Modeling Natural Language Processing Organism Pharmacologic Substance Plasmids Polymers Positioning Attribute Production Protein Conformation Proteins Recombinant Proteins Reporting Research Personnel Risk Running Signal Transduction System Techniques Technology Textiles Training Translations Treatment Efficacy Variant Vegan Diet cost drug discovery experimental study feeding hands-on learning improved in vitro Assay meetings prevent process optimization protein expression protein folding protein function protein misfolding r-hGH-M scale up tool vector

项目摘要

Project Summary/Abstract: Recombinant proteins have a wide range of applications, from pharmaceutical products, drug discovery, protein-based polymers for drug delivery, antibodies enzymes, and sustainable technologies such as textiles and vegan food production such as the impossible burger. Towards meeting these increased demands by replacing batch culture with continuous culture reduces overhead costs, batch-to-batch variation, and increases protein production. Further, towards maximizing recombinant protein, yield computational techniques for gene engineering, such as codon optimization, use synonymous codon changes to increase protein production. Although codon optimization increases protein production in specific systems, synonymous changes to a gene sequence can cause unexpected detrimental results to the protein, such as protein misfolding, decreased protein yield, and vector loss. Therefore, codon optimization may not provide an optimal strategy for increasing protein production in batch culture and may introduce risk when scaling to continuous culture. 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 that our AI-based codon harmonization model could predict the E. Coli synonymous codon usage with 73% accuracy, significantly above prior reports. Using this AI-based approach to gene engineering will provide an optimal strategy for increasing protein production in batch culture and continuous culture in addition to de-risk scaling up to continuous culture.

项目概要/摘要：重组蛋白具有广泛的应用，从医药产品、药物发现，用于药物递送的蛋白质聚合物，抗体酶，以及可持续的比如纺织品和素食食品生产，比如不可能的汉堡。朝向通过用连续培养代替分批培养来满足这些增加的需求，间接成本、批次间差异，并增加蛋白质产量。此外，向最大化重组蛋白，产生用于基因工程的计算技术，例如密码子优化，使用同义密码子改变来增加蛋白质产量。虽然密码子优化增加了特定系统中的蛋白质产量，基因序列可对蛋白质造成意想不到的有害结果，例如蛋白质错误折叠、蛋白质产量降低和载体损失。因此，密码子优化可能不提供了在分批培养中增加蛋白质产量的最佳策略，风险时扩展到连续培养。CFDRC利用最先进的自然语言处理技术，以了解同义密码子如何被目标生物体使用，并应用这种学习到基因工程。我们证明了我们基于人工智能的密码子协调模型可以预测E.大肠杆菌同义密码子使用的准确率为73%，显著高于先前的报告。将这种基于人工智能的方法用于基因工程将提供一种最佳策略除了降低风险外，还用于增加分批培养和连续培养中的蛋白质产量扩大到连续培养。