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：
10687279
负责人：
Andrew Kruse
金额：
$ 166.79万
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/10687279
关键词：
Acetylcholine Affinity Animals Antibodies Antibody Affinity Antibody Formation Antigen Presentation Antigen Targeting Antigen-Presenting Cells Antigens Architecture Area Back Biogenic Amine Receptors Biological Sciences Biomedical Research Cell Surface Receptors Cells Central Nervous System Chemistry Clinic Collection Communities Custom Cytometry Data Data Set Democracy Detergents Diagnostic Directed Molecular Evolution Docking Dopamine Elements Engineering 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 Neurobiology Neurosciences Neurotransmitters Nobel Prize Outcome Pathogen detection Phage Display Pharmaceutical Preparations Pheromone Play Process Production Productivity Proliferating Protein Engineering Proteins Proteome Public Health Reagent Research Research Personnel Role Signal Transduction Specificity Speed Surface System Techniques Testing Therapeutic Training Tube Update V(D)J Recombination Western Blotting Yeasts addiction antibody engineering antibody libraries antigen binding biomarker discovery cancer therapy cost crowdsourcing decision research design empowerment epidemic response experimental study follow-up improved in vivo innovation insight interest machine learning algorithm machine learning model 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.!

项目概要/摘要单克隆抗体在生命科学中的重要性怎么强调都不为过。抗体是生物医学的重要工具。研究和诊断（例如蛋白质印迹、免疫沉淀、细胞计数、生物标志物发现和组织学）增长最快的治疗方法之一，是癌症治疗中无数新策略的基础，例如不幸的是，目前产生定制治疗的方法。抗体，包括动物免疫和噬菌体展示，速度慢、成本高，大多数研究人员无法获得，而且通常我们提出了自主进化的酵母展示抗体（AEGYS），这是一种连续且持续的系统。只需简单培养酵母细胞即可快速开发针对定制抗原的高质量抗体。相信这可以通过结合尖端的生成机器学习算法进行抗体库设计来实现具有体内连续进化的新技术和我们将设计的酵母抗原呈递细胞。如果成功，AEGYS 将通过单克隆抗体对整个生物医学产生变革性影响生成到一个快速、可扩展且可访问的过程，任何具有标准分子生物学能力的实验室都可以我们预计，只需用抗原“免疫”酵母细胞试管即可按需生成定制抗体。抗体生成的民主化也将导致众包抗体序列数据的爆炸式增长这将训练我们的机器学习算法为 AEGYS 设计更好的抗体库，从而开始一个良性循环。我们自己将使用 AEGYS 生成一组针对生物胺的亚型和构象特异性纳米抗体受体，包括那些对乙酰胆碱、肾上腺素、多巴胺和其他神经递质有反应的受体，这样我们就可以了解它们在神经生物学和成瘾中的作用。！

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction.

ProteinGym：蛋白质设计和健身预测的大规模基准。

DOI：
发表时间：
2023-12-08
期刊：
影响因子：
0
作者：
Notin, Pascal;Kollasch, Aaron W;Ritter, Daniel;van Niekerk, Lood;Paul, Steffanie;Spinner, Hansen;Rollins, Nathan;Shaw, Ada;Weitzman, Ruben;Frazer, Jonathan;Dias, Mafalda;Franceschi, Dinko;Orenbuch, Rose;Gal, Yarin;Marks, Debora S
通讯作者：
Marks, Debora S

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders.

人类蛋白质组的深度生成模型揭示了一百多个与罕见遗传疾病有关的新基因。

DOI：
发表时间：
2023-11-28
期刊：
影响因子：
0
作者：
Orenbuch, Rose;Kollasch, Aaron W;Spinner, Hansen D;Shearer, Courtney A;Hopf, Thomas A;Franceschi, Dinko;Dias, Mafalda;Frazer, Jonathan;Marks, Debora S
通讯作者：
Marks, Debora S

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders.

人类蛋白质组的深度生成模型揭示了一百多个与罕见遗传疾病有关的新基因。

DOI：
发表时间：
2024-01-04
期刊：
影响因子：
0
作者：
Orenbuch, Rose;Kollasch, Aaron W;Spinner, Hansen D;Shearer, Courtney A;Hopf, Thomas A;Franceschi, Dinko;Dias, Mafalda;Frazer, Jonathan;Marks, Debora S
通讯作者：
Marks, Debora S

ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers.

ProteinNPT：使用非参数转换器改进蛋白质特性预测和设计。

DOI：
发表时间：
2023-12-07
期刊：
影响因子：
0
作者：
Notin, Pascal;Marks, Debora S;Weitzman, Ruben;Gal, Yarin
通讯作者：
Gal, Yarin

Protein design using structure-based residue preferences.

使用基于结构的残基偏好进行蛋白质设计。

DOI：
发表时间：
2024-02-22
期刊：
影响因子：
16.6
作者：
Ding, David;Shaw, Ada Y;Sinai, Sam;Rollins, Nathan;Prywes, Noam;Savage, David F;Laub, Michael T;Marks, Debora S
通讯作者：
Marks, Debora S