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

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。 电子计算机中针对外源或非自身‘抗原性’蛋白的序列。免疫球蛋白链重排可以 可能编码大约1016种不同的抗体重链和轻链序列变体。然而， 通常只有一小部分序列空间被用于进化针对外源蛋白的抗体。 计算上的挑战是从抗原结构的模型到预测一组抗体 能与抗原紧密结合的链序列。如果得到解决，在不到24小时内搬家是可能的。 从首次提出的一种新型病毒蛋白的冷冻电子显微镜结构出发，提出了一套有效的抗体样蛋白 待测试的分子候选者。为了解决这一问题，本项目旨在开发一种深度学习 将热力学、量子力学(密度泛函)和局域结构作为输入的体系结构- 基于抗原及其同源抗体的网络拓扑特征，并将其输出 各自的结合亲和力常数。 我们将设计一个生成性对抗网络(GAN)，我们认为它是唯一适合于基于回归的网络 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，直到 最近很难买到。一种新近报道的单一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 到抗原表位。 对于具体的项目目标，在任务1中，将使用Libra-seq来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(对照)， 通过计算/分子模拟选择，并优先考虑SARS-CoV-2尖峰蛋白表位(但 不限于这些)，注入到老鼠模型中以生成大的训练集；在任务2中，这些训练 集合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)，这将用于训练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 科学来解决一个普遍的生物工程问题。 该项目的结果有望为经过严格测试的全自动机器奠定基础- 一种学习系统，可以从一种新病毒的结构中快速产生合成抗体候选 蛋白质，可以增强应对未来大流行的快速反应能力。有针对性地发展的能力 针对非传染性疾病或慢性病的抗体治疗，以及基于抗体的工业生产 如果这个项目取得成功，酶也将得到极大的增强。 团队：这个多机构研究项目的团队负责人包括一名计算机科学家，一名蛋白质 结晶学家、免疫学家和分子生物学家。 1

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