Machine learning assisted construct design to accelerate protein production.

机器学习辅助构建设计以加速蛋白质生产。

• 批准号: 74918
• 金额: $ 3.62万
• 依托单位: UNIVERSITY OF CAMBRIDGE
• 依托单位国家: 英国
• 项目类别: Study
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=74918
• 关键词: Machine; learning; assisted; construct; design

Drug discovery and biomedical research in academia relies heavily on the production of proteins for example in bioassays, high-throughput screening for novel active compounds and the determination of protein crystal structures. _However, producing and delivering a specific protein to partners in sufficient quality and quantity is often the first and rate-limiting step in the initiation of drug discovery projects._Protein constructs are currently being designed manually based on the expertise of experienced scientists and require repeated rounds of trial and error optimisations, making this a labour-intensive process. Therefore, the focus of this project will be to accurately predict protein production yield from its constituent amino acid sequences.To achieve this goal Dr. Dilrini De Silva, a bioinformatician with extensive experience in genomic research will be seconded to AstraZeneca for a period of six months. She will be operating at the intersection of Quantitative Biology (a data science department) and Protein Production teams within AstraZeneca.

学术界的药物发现和生物医学研究在很大程度上依赖于蛋白质的生产，例如在生物测定中，高通量筛选新的活性化合物和确定蛋白质晶体结构。然而，以足够的质量和数量生产并向合作伙伴提供特定蛋白质通常是启动药物发现项目的第一步和限速步骤。目前，蛋白质构建体是根据经验丰富的科学家的专业知识手动设计的，需要反复的试错优化，这是一个劳动密集型的过程。因此，本项目的重点将是从其组成氨基酸序列准确预测蛋白质的产量。为了实现这一目标，在基因组研究方面具有丰富经验的生物信息学家Dilrini De Silva博士将被借调到阿斯利康，为期六个月。她将在阿斯利康的定量生物学（数据科学部门）和蛋白质生产团队的交叉点工作。