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 免疫学的“圣杯”之一是能够直接预测紧密结合的可变链抗体 计算机模拟中针对外来或非自身“抗原”蛋白质的序列可以进行免疫球蛋白链重排。 可能编码大约 1016 种不同的抗体重链和轻链序列变体。 通常只有一小部分序列空间可用于进化针对外来蛋白质的抗体。 计算挑战是从抗原结构模型到预测一组抗体 能够与抗原紧密结合的链序列如果被解决，可能会在 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到推进一系列有效的抗体样 为了解决这个问题，该项目旨在开发一种深度学习方法。 将采用热力学、量子力学（密度泛函）和局部结构作为输入的架构- 基于抗原及其同源抗体的网络拓扑特征，并将输出它们 各自的结合亲和力常数。 我们将设计一个生成对抗网络（GAN），我们认为它特别适合基于回归的 免疫系统的机器学习方法，发现表位和可变链之间的关联 这种方法需要大量的抗原和同源抗体序列数据流，直到 最近很难获得最近描述的单一 B 细胞受体 (BCR) 特异性标记方法。 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 LIBRA- seq）可以快速分离和测序能够高选择性结合的BCR可变链编码区 至抗原表位。 为了实现具体的项目目标，在任务 1 中，LIBRA-seq 将用于快速识别和生成候选者 响应特定线性和非线性表位的免疫球蛋白编码序列（相对于对照）， 通过计算/分子模型进行选择，并优先考虑 SARS-CoV-2 刺突蛋白表位（但 在任务 2 中，这些训练 集以及公共数据库中已有的其他数据集将生成一系列结构特征 （如上所述），将用于训练任务 3 中的 GAN；预测表位-抗体相互作用 将通过合成抗体和噬菌体展示系统的直接实验来验证。 策略结合了进化生物学、基因组学、结构化学和计算机的基本原理 科学来解决一般生物工程问题。 该项目的结果预计将为经过严格测试的全自动机器奠定基础 可以根据新型病毒的结构快速生成合成抗体候选物的学习系统 蛋白质，可以增强针对未来大流行的快速反应能力，开发有针对性的能力。 针对非传染性或慢性疾病的抗体疗法，以及基于抗体的工业生产 如果该项目成功的话，酶的含量也将得到显着增强。 团队：这个多机构研究项目的团队领导包括一名计算机科学家、一名蛋白质科学家 晶体学家、免疫学家和分子生物学家。 1