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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.!

项目总结/文摘