# Rapid response for pandemics: single cell sequencing and deep learning to predict antibody sequences against an emerging antigen > **NIH NIH R01** · KECK GRADUATE INST OF APPLIED LIFE SCIS · 2021 · $1,851,627 ## Abstract 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 candid... ## Key facts - **NIH application ID:** 10274223 - **Project number:** 1R01AI169543-01 - **Recipient organization:** KECK GRADUATE INST OF APPLIED LIFE SCIS - **Principal Investigator:** Jeniffer Bertha Hernandez - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $1,851,627 - **Award type:** 1 - **Project period:** 2021-09-16 → 2025-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10274223 ## Citation > US National Institutes of Health, RePORTER application 10274223, Rapid response for pandemics: single cell sequencing and deep learning to predict antibody sequences against an emerging antigen (1R01AI169543-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10274223. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*