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Self-driving laboratories for autonomous exploration of protein sequence space

用于自主探索蛋白质序列空间的自动驾驶实验室

基本信息

批准号：
10717598
负责人：
Philip Anthony Romero
金额：
$ 34.92万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
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：
2023-09-19 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10717598
关键词：
Address Amino Acid Sequence Antibodies Artificial Intelligence Automation Automobile Driving Biochemical Biological Response Modifier Therapy Biotechnology Cells Chemicals Chemistry Complex Computer Models Computer software Consumption DNA biosynthesis Data Decision Making Directed Molecular Evolution Disease Disparate Engineering Environment Enzymes Evolution Feedback Food Genes Growth Human Intervention Laboratories Learning Measurement Medicine Metadata Methodology Modality Modeling Mutation Nature Neighborhoods Peptide Hydrolases Peptide Signal Sequences Pharmacologic Substance Polymers Process Protein Biosynthesis Protein Engineering Proteins Reproducibility Research Personnel Signal Pathway Source Structure System Testing Therapeutic Time Uncertainty Variant Walking artificial intelligence algorithm automated algorithm cell behavior chemical reaction data sharing deep learning design enzyme activity experimental study fitness high dimensionality high throughput screening imaging agent improved intelligent agent large datasets molecular diagnostics molecular imaging open source robotic system small molecule success synthetic biology therapeutic enzyme

项目摘要

PROJECT SUMMARY/ABSTRACT We propose to develop fully autonomous “self-driving laboratories” to rapidly engineer enzymes for broad applications in biomedicine and biocatalysis. Our approach mimics the methodology of a protein engineering researcher with an AI layer that builds an understanding of protein sequence-structure-function and plans experiments to test specific protein design hypotheses, and a robotic system that experimentally tests designed proteins by synthesizing genes, expressing proteins, and performing biochemical measurements of enzyme activity. Seamless integration between the intelligent agent and experimental automation enables fully autonomous design-test-learn cycles to understand and optimize the sequence-function landscape. Self- driving laboratories will revolutionize the fields of biomolecular engineering and synthetic biology by automating highly inefficient, time consuming, and laborious protein engineering campaigns, enabling rapid turnaround, and allowing researchers to focus efforts on important downstream applications.

项目总结/摘要我们建议开发完全自主的“自动驾驶实验室”，以快速设计酶，在生物医学和生物催化中的应用。我们的方法模仿了蛋白质工程的方法研究人员与人工智能层，建立蛋白质序列的理解-结构-功能和计划测试特定蛋白质设计假设的实验，以及通过实验测试的机器人系统通过合成基因、表达蛋白质和进行生物化学测量来设计蛋白质，酶活性智能代理和实验自动化之间的无缝集成，自主设计-测试-学习循环，以理解和优化序列-功能景观。自我- 驾驶实验室将通过自动化，非常低效、耗时和费力的蛋白质工程活动，能够快速周转，并使研究人员能够集中精力研究重要的下游应用。