Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolution

通过机器学习和持续进化，使抗体生成快速、可扩展且民主

基本信息

批准号：
10021311
负责人：
Andrew Kruse
金额：
$ 169.06万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-10 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10021311
关键词：
Acetylcholine Affinity Animals Antibodies Antibody Affinity Antibody Formation Antigen Targeting Antigen-Presenting Cells Antigens Architecture Area Back Biogenic Amine Receptors Biological Sciences Biomedical Research Cell Surface Receptors Cells Chemistry Clinic Collection Communities Cultured Cells Custom Cytometry Data Data Set Detergents Diagnostic Directed Molecular Evolution Docking Dopamine Elements Engineering Epidemic Epinephrine Evolution Explosion G-Protein-Coupled Receptors Generations Genes Genetic Histology Human Hybridomas Image Immune checkpoint inhibitor Immune system Immunization Immunize Immunoglobulin Fragments Immunoprecipitation Libraries Machine Learning Medical Research Medicine Methods Modeling Molecular Molecular Biology Molecular Conformation Monoclonal Antibodies Neuraxis Neurobiology Neurosciences Neurotransmitters Nobel Prize Outcome Pathogen detection Phage Display Pharmaceutical Preparations Pheromone Play Problem Solving Process Production Protein Engineering Proteins Proteome Public Health Reagent Research Research Personnel Role Signal Transduction Specificity Speed Surface System Techniques Testing Therapeutic Therapeutic Studies Training Tube Update V(D)J Recombination Western Blotting Yeasts addiction antibody engineering antibody libraries antigen binding base biomarker discovery cancer therapy cost crowdsourcing decision research design empowered experimental study follow-up improved in vivo innovation insight interest machine learning algorithm nanobodies new technology novel receptor response scaffold structural biology tool

项目摘要

Project Summary/Abstract It is hard to overstate the importance of monoclonal antibodies in the life sciences. Antibodies are critical tools in biomedical research and diagnostics (e.g. western blotting, immunoprecipitation, cytometry, biomarker discovery, and histology), are one of the most rapidly growing class of therapeutics, and are the basis for myriad new strategies in cancer therapy, such as checkpoint inhibitors that are revolutionizing treatment. Unfortunately, current methods for the generation of custom antibodies, including animal immunization and phage display, are slow, costly, inaccessible to most researchers, and often unsuccessful. We propose Autonomously EvolvinG Yeast-displayed antibodieS (AEGYS), a system for the continuous and rapid evolution of high-quality antibodies against custom antigens that requires only the simple culturing of yeast cells. We believe this can be achieved by combining cutting-edge generative machine learning algorithms for antibody library design with a new technology for in vivo continuous evolution and a yeast antigen-presenting cell that we will engineer. If successful, AEGYS should have a transformative impact across the whole of biomedicine by turning monoclonal antibody generation into a rapid, scalable, and accessible process where any lab with standard molecular biology capabilities can generate custom antibodies on demand simply by “immunizing” a test tube of yeast cells with an antigen. We anticipate that this democratization of antibody generation will also result in an explosion of crowdsourced antibody sequence data that will train our machine learning algorithms to design better antibody libraries for AEGYS, starting a virtuous cycle. We ourselves will use AEGYS to generate a panel of subtype- and conformation-specific nanobodies against biogenic amine receptors including those that respond to acetylcholine, adrenaline, dopamine, and other neurotransmitters, so that we can understand their role in neurobiology and addiction.!

项目摘要/摘要很难夸大单克隆抗体在生命科学中的重要性。抗体是生物医学的关键工具研究和诊断（例如，蛋白质印迹，免疫沉淀，细胞仪，生物标志物发现和组织学）是理论阶段最快，是癌症治疗中无数新策略的基础，例如正在革新治疗的检查点抑制剂。不幸的是，当前生成自定义的方法包括动物免疫抑制和噬菌体显示在内不成功。我们提出了自主发展的酵母菌脱落抗体（AEGYS），这是一种连续和的系统针对自定义抗原的高质量抗体的快速演变仅需要酵母细胞的简单文化。我们相信这可以通过结合抗体库设计的尖端通用机器学习算法来实现使用新技术，用于体内连续进化和我们将设计的酵母抗原细胞。成功的AEGY应该通过转动单克隆抗体对整个生物医学产生变革性影响在任何具有标准分子生物学能力的实验室都可以生成快速，可扩展和可访问的过程仅通过“免疫”带有抗原的酵母细胞试管来生成自定义的抗体。我们期待抗体产生的民主化也将导致众包抗体序列数据的爆炸这将训练我们的机器学习算法，以设计为AEGYS的更好的抗体库，开始虚拟周期。我们我们自己将使用AEGYS生成一个针对生物胺的亚型和构象特异性纳米体包括对乙酰胆碱，肾上腺素，多巴胺和其他神经递质的接收器，以便我们可以了解它们在神经生物学和成瘾中的作用。