Rapid response for pandemics: single cell sequencing and deep learning to predict antibody sequences against an emerging antigen

快速应对流行病：单细胞测序和深度学习预测针对新兴抗原的抗体序列

• 批准号: 10274223
• 负责人: Jeniffer Bertha Hernandez
• 金额: $ 185.16万
• 依托单位: KECK GRADUATE INST OF APPLIED LIFE SCIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-16 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10274223
• 关键词: Affinity; Amino Acid Sequence; Antibodies; Antibody Formation; Antibody Specificity; Antibody Therapy; Antigen-Antibody Complex; Antigens; Architecture; B-Cell Antigen Receptor; B-Cell Receptor Binding; B-Lymphocytes; Base Sequence; Binding; Biological; Biology; Cells; Chronic Disease; Code; Computer Models; Computers; Computing Methodologies; Coupled; Data; Data Set; Databases; Degenerative Disorder; Development; Diagnosis; Economics; Electrons; Engineering; Enzymes; Epitopes; Equilibrium; Foundations; Future; Genes; Genomics; Goals; Hour; Immune system; Immunize; Immunoassay; Immunoglobulins; Immunologist; Immunology; Industrialization; Ligands; Light; Link; Machine Learning; Malignant Neoplasms; Measurable; Mechanics; Methods; Microscopic; Modeling; Molecular; Molecular Biology; Molecular Computations; Mus; Nature; Network-based; Neural Network Simulation; Output; Passive Immunotherapy; Phage Display; Phase; Play; Problem Solving; Process; Production; Proteins; Readiness; Reagent; Research Project Grants; SARS-CoV-2 antigen; SARS-CoV-2 spike protein; Savings; Scientist; Series; Specificity; Structural Chemistry; Structural Models; Structure; Surface Plasmon Resonance; System; Testing; Therapeutic; Therapeutic antibodies; Thermodynamics; Time; Training; Vaccines; Validation; Variant; Viral; Viral Antigens; Viral Proteins; Work; base; combat; computer science; data streams; database structure; deep learning; deep neural network; deep sequencing; density; design; experimental study; high dimensionality; in silico; innovation; insight; large datasets; machine learning method; molecular modeling; mouse model; neutralizing antibody; novel; novel virus; pandemic disease; pandemic preparedness; pathogen; physical property; protein structure; quantum; response; scaffold; simulation; single cell sequencing; synthetic antibodies; therapeutic evaluation; three dimensional structure

ABSTRACT One of the “holy grails” in immunology is to be able to directly predict tight-binding variable chain antibody sequences in silico against foreign or non-self `antigenic' proteins. Immunoglobulin chain rearrangement can potentially encode approximately 1016 different variants of antibody heavy and light chain sequences. However, only a small fraction of the sequence space is generally accessed for evolving antibodies against foreign proteins. The computational challenge is to go from a model of the structure of an antigen to predicting a set of antibody chain sequences that can bind tightly to the antigen. If solved, it might be possible to move in less than 24 hours from the first cryo-electron-microscopic structure of a novel viral protein to advance a set of potent antibody-like molecular candidates for testing. Towards solving this problem, this project aims to develop a deep learning architecture that will take as input thermodynamic, quantum mechanical (density functional), and local structure- based network topographical features of the antigens and their cognate antibodies, and will output their respective binding affinity constants. We will design a generative adversarial network (GAN), which we think is uniquely suited for regression-based ML approaches for the immune system, to discover associations between the epitope and the variable chain features. This approach requires a large data stream of antigen and cognate antibody sequences, which until recently was difficult to obtain. A recently described single B-cell receptor (BCR) specific tagging method coupled with single cell deep sequencing (“linking B cell receptor to antigen specificity through sequencing” or LIBRA- seq) can rapidly isolate and sequence the BCR variable chain coding regions that can bind with high selectivity to antigenic epitopes. Towards the specific project goals, in Task 1, LIBRA-seq will be used to rapidly identify and generate candidate immunoglobulin coding sequences in response to specific linear and nonlinear epitopes (against controls), chosen through computational/molecular modeling and prioritized with SARS-CoV-2 Spike protein epitopes (but not restricted to these), injected into a mouse model, to generate large training sets; in Task 2, these training sets, along with other data sets already available in public databases, will generate a series of structural features (described above), which will be used to train the GAN; in Task 3, the predicted epitope-antibody interactions will be validated by direct experiments with synthetic antibody and phage-display systems. Thus, the proposed strategy combines foundational principles in evolutionary biology, genomics, structural chemistry, and computer science to the solution of a general biological engineering problem. Results from this project are expected to lay the foundations for a rigorously tested and fully automated machine- learning system that could rapidly generate synthetic antibody candidates from the structure of a novel virus protein, which can enhance the rapid response ability against a future pandemic. The ability to develop targeted antibody therapy against non-infectious or chronic diseases, and on the production of antibody-based industrial enzymes, will also be dramatically enhanced if this project were to be successful. The team: The team-leads of this multi-institutional research project comprise a computer scientist, a protein crystallographer, an immunologist, and a molecular biologist. 1

抽象的 免疫学中的“圣杯”之一是能够直接预测紧密结合变量链抗体 针对异物或非自身抗原蛋白的硅序列。免疫球蛋白链重排可以 潜在地编码大约1016种抗体重链和轻链序列的不同变体。然而， 通常只能访问序列空间的一小部分，以抗异国蛋白质的抗体。 计算挑战是从抗原结构的模型进行预测一组抗体 可以与抗原紧密结合的链序列。如果解决，可能会在不到24小时内移动 从新型病毒蛋白的第一个冷冻电子显微镜结构开始，以推动一组潜在的抗体样 用于测试的分子候选者。为了解决这个问题，该项目旨在发展深入学习 将采用输入热力学，量子机械（密度功能）和局部结构的结构 - 基于抗原及其同源抗体的网络地形特征，并将输出 主要结合亲和力常数。 我们将设计一个通用的对抗网络（GAN），我们认为它非常适合基于回归 ML接种系统的方法，以发现表位与变量链之间的关联 特征。这种方法需要大量的抗原和同源抗体序列的数据流，直到 最近描述的单个B细胞接收器（BCR）特定标记方法耦合 单细胞深度测序（“通过测序将B细胞受体与抗原特异性联系起来”或库 SEQ）可以快速隔离并对BCR变量链编码区进行序列，以高选择性结合 抗原表位。 朝着特定的项目目标，在任务1中，Libra-Seq将用于快速识别和生成候选人 免疫球蛋白编码序列响应特定的线性和非线性表位（反对）， 通过计算/分子建模选择，并用SARS-COV-2尖峰蛋白表位优先（但是 不限于将其注入鼠标模型，以生成大型训练集；在任务2中，这些培训 集合以及公共数据库中已经可用的其他数据集将生成一系列结构功能 （如上所述），将用于训练甘恩；在任务3中，预测的表位 - 抗体相互作用 将通过合成抗体和噬菌体播放系统的直接实验来验证。那，提议 策略组合进化生物学，基因组学，结构化学和计算机的基础原理 科学解决通用生物工程问题的解决方案。 预计该项目的结果将为经过严格测试且完全自动化的机器奠定基础。 可以快速从新病毒结构中生成合成抗体的学习系统 蛋白质可以增强对未来大流行的快速反应能力。发展目标的能力 针对非感染或慢性疾病的抗体疗法以及基于抗体的工业的生产 如果该项目成功，酶也将动态增强。 团队：这个多机构研究项目的团队领导者建立了计算机科学家，一种蛋白质 晶体学学，免疫学家和分子生物学家。 1