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

摘要 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 计算机模拟的抗外来或非自身“抗原性”蛋白的序列。免疫球蛋白链重排可以 可能编码抗体重链和轻链序列的大约1016种不同变体。然而，在这方面， 通常只有一小部分序列空间可用于进化抗外源蛋白的抗体。 计算的挑战是从抗原的结构模型到预测一组抗体 可以紧密结合抗原的链序列。如果解决了，可能在24小时内移动 从一种新型病毒蛋白的第一个冷冻电子显微镜结构， 用于测试的分子候选物。为了解决这个问题，该项目旨在开发一种深度学习 体系结构，将作为输入热力学，量子力学（密度泛函），和本地结构- 基于抗原及其同源抗体的网络拓扑特征，并将输出其 各自的结合亲和力常数。 我们将设计一个生成对抗网络（GAN），我们认为它非常适合基于回归的 免疫系统的ML方法，以发现表位和可变链之间的关联 功能.这种方法需要大量的抗原和同源抗体序列的数据流， 最近很难获得。最近描述的单个B细胞受体（BCR）特异性标记方法， 用单细胞深度测序（“通过测序将B细胞受体与抗原特异性连接”或LIBRA- seq）可以快速分离和测序能够以高选择性结合的BCR可变链编码区 抗原表位。 针对具体的项目目标，在任务1中，LIBRA-seq将用于快速识别和生成候选人 免疫球蛋白编码序列对特异性线性和非线性表位的应答（对照）， 通过计算/分子建模选择并优先考虑SARS-CoV-2刺突蛋白表位（但 不限于这些），注射到小鼠模型中，以生成大型训练集;在任务2中，这些训练 沿着公共数据库中已有的其他数据集，将产生一系列结构特征 （如上所述），其将用于训练GAN;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展示系统的直接实验进行验证。因此，拟议的 战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 解决一般生物工程问题的科学。 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- 一个学习系统，可以从一种新病毒的结构中快速生成合成抗体候选物。 蛋白质，这可以提高对未来大流行病的快速反应能力。有针对性地开发能力 针对非传染性或慢性疾病的抗体治疗，以及基于抗体的工业生产 如果这个项目成功的话，酶也将得到极大的提高。 团队：这个多机构研究项目的团队领导包括一位计算机科学家，一位蛋白质科学家， 晶体学家、免疫学家和分子生物学家。 1