DNA 3.0: Developing novel enzymes for DNA synthesis with deep learning and combinatorial genetics

DNA 3.0：利用深度学习和组合遗传学开发用于 DNA 合成的新型酶

• 批准号: 10304760
• 负责人: Helge Zieler
• 金额: $ 5.2万
• 依托单位: PRIMORDIAL GENETICS, INC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-17 至 2022-01-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10304760
• 关键词: Artificial Intelligence; Biological Process; Biotechnology; Cells; Chemicals; Collection; Cost efficiency; Cystic Fibrosis; DNA; DNA biosynthesis; DNA-Directed DNA Polymerase; Data Set; Databases; Diagnosis; Diagnostic; Enzymes; Feasibility Studies; Genes; Genetic; Genetic Diseases; HIV; Heart Diseases; In Vitro; Industrialization; Industry; Length; Malignant Neoplasms; Methods; Molecular Machines; Mutation; Nucleotides; Oligonucleotides; Phase; Play; Polymerase; Population; Prevention; Randomized; Reagent; Reporting; Research Project Grants; Scanning; Small Business Innovation Research Grant; Technology; Testing; Therapeutic; Training; Variant; Work; base; combinatorial; cost; deep learning; genetic technology; high throughput screening; human disease; improved; machine learning algorithm; next generation; novel; prediction algorithm; synthetic biology; synthetic construct; tool

Project Summary/Abstract DNA synthesis has played a key role in the biotechnology revolution. The ready availability of synthetic DNA oligonucleotides and of genes assembled from them, has been invaluable for elucidating and unlocking biological function and enabling the new field of synthetic biology which can create novel cells, enzymes, therapeutics, diagnostics and other reagents of commercial value. Despite this impact, DNA synthesis uses chemical strategies developed over 30 years ago which are costly and limited to molecules of 200 nucleotides or less in length. Next-generation enzymatic DNA synthesis technologies are being explored that use template- independent DNA polymerases (TIDPs) for controlled addition of nucleotides to a growing DNA strand. Although advances have been reported recently, enzymatic DNA synthesis is still limited by the low efficiency of available TIDPs, and specifically by the relative inability of these polymerases to incorporate 3'-blocked nucleotides. In this Phase I Small Business Innovation Research (SBIR) project, Primordial Genetics Inc, a synthetic biology company with differentiated combinatorial genetic technology, and Denovium Inc., an artificial intelligence company pioneering novel Al methods for genetic discovery, are joining forces to develop novel and highly efficient TIDPs for enzymatic DNA synthesis in vitro. Denovium will use their computational capabilities based on machine learning algorithms to discover novel TIDPs with the desired activities from proprietary and public databases. Denovium will also perform proprietary artificial intelligence (AI) scans to determine the functional impact of all possible mutations on the selected TIDPs. Primordial Genetics will synthesize and express the resulting collection of sequences, and test them in vitro to identify the most active enzymes. The best 2 enzymes will be diversified using Primordial Genetics' proprietary Function Generator technology and other randomized diversification methods. Populations of genes encoding enzyme variants will be screened with ultra-high-throughput screens to identify the most active enzymes. The dataset resulting from this work will be used to train Denovium's sequence prediction algorithm to accelerate further enzyme improvements in Phase II. The proposed work is a feasibility study for isolating and developing novel enzymes suitable for enzymatic DNA synthesis, and also for creating a pipeline of enzyme optimization tools. The enzymes discovered and in this work will be directly useful for enzymatic DNA synthesis applications, and can be licensed or sold to leading DNA and gene manufaturers.

项目摘要/摘要 DNA合成在生物技术革命中发挥了关键作用。现成的 合成DNA寡核苷酸和从中组装的基因的基因，对于 阐明和解锁生物学功能，并实现合成生物学的新领域，该领域 可以创建新颖的细胞，酶，治疗学，诊断和其他商业价值试剂。 尽管有这种影响，但DNA合成使用了30年前开发的化学策略 成本高昂，限于200个核苷酸的分子。 正在探索下一代酶DNA合成技术，该技术使用模板 - 独立的DNA聚合酶（TIDPS），用于在生长的DNA中受控添加核苷酸 链。尽管最近报告了进展，但酶DNA合成仍然受到 可用的Tidps的低效率，特别是这些聚合酶的相对无能为力 掺入3'Blocked核苷酸。 在这一阶段，I小型企业创新研究（SBIR）项目，原始遗传学公司 合成生物学公司具有分化的组合遗传技术，以及Denovium Inc.， 人工智能公司开创了遗传发现的小说Al方法，正在加入 在体外开发新型且高效的TIDP来进行酶促DNA合成的力。 Denovium将根据机器学习算法使用其计算功能 通过专有和公共数据库中所需的活动发现新颖的Tidps。 Denovium 还将执行专有人工智能（AI）扫描以确定所有人的功能影响 在选定的Tidps上可能发生突变。原始遗传学将合成并表达 产生的序列收集，并在体外测试它们以鉴定最活跃的酶。最好的 2酶将使用原始遗传学的专有功能生成器技术多样化 和其他随机多元化方法。编码酶变体的基因种群将 用超高通量屏幕筛选以识别最活跃的酶。数据集 这项工作将用于训练Denovium的序列预测算法以加速 II期的进一步改进。 拟议的工作是一项可行性研究，用于隔离和开发适合于 酶促DNA合成，还用于创建酶优化工具的管道。这 发现的酶和在这项工作中将直接用于酶促DNA合成应用， 并且可以被许可或出售给领先的DNA和基因制造商。