# Coupling Machine Learning with Agent-Based Modeling to Design a Universal Influenza Vaccine

> **NIH NIH R21** · UNIVERSITY OF COLORADO · 2023 · $147,093

## Abstract

PROJECT SUMMARY/ABSTRACT
Influenza is a devasting illness that causes up to 61,000 deaths each year in the United States, and up to 650,000
deaths each year globally. While influenza vaccines exist, they must be modified annually due to rapid sequence
evolution of hemagglutinin (HA), the oft-targeted influenza surface protein (antigen, Ag). A vaccine formulated to
be effective against diverse HA sequences would have greatly increased efficacy and would constitute a
‘universal’ influenza vaccine that could save many lives. To this end, there exist multiple groups of residues on
the surface of HA that do not mutate as readily, due to their functional role in allowing the virus to attach to and
enter host cells. The receptor binding site (RBS) contains such ‘conserved’ residues, which would be ideal targets
for a universal influenza vaccine; however, the high sequence diversity of the surrounding ‘variable’ residues
renders it difficult for antibodies (Abs) to bind to the conserved residues with high affinity. We hypothesize a
universal influenza vaccine should comprise multiple immunizations of HA-based Ags with increasingly
diverse sequences at variable positions surrounding conserved sites. We expect this approach to
provide a continuous driving force for Abs to target conserved HA residues while simultaneously
coaching them on how to tolerate or altogether avoid binding to variable residues. To test this hypothesis,
we will adapt our computational model of affinity maturation (AM) – the process by which antibodies mature in
vivo – geared towards evolving anti-HIV Abs, into a robust tool for Ag design against the conserved residues of
the RBS. This model will incorporate important disease features, such as the crucial role of stabilizing framework
mutations in the evolution of anti-influenza Abs. To efficiently traverse the vast sequence landscape of the HA-
based Ags, we will employ deep reinforcement learning (DRL) to steer the AM process towards the optimal Ag
sequences. We will first test this unique coupling of machine learning with stochastic biological modeling on our
recently developed AM model with coarse-grained resolution to enable efficient optimization of algorithmic
parameters. We will then apply this framework to our realistic AM model towards the design of real HA-based
sequences for a universal influenza vaccine. Optimized HA sequences will be directly compared against naturally
evolved anti-influenza Ab sequences with high potency and neutralization breadth against multiple influenza
subtypes.

## Key facts

- **NIH application ID:** 10619595
- **Project number:** 5R21AI169364-02
- **Recipient organization:** UNIVERSITY OF COLORADO
- **Principal Investigator:** Kayla Sprenger
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $147,093
- **Award type:** 5
- **Project period:** 2022-05-09 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10619595

## Citation

> US National Institutes of Health, RePORTER application 10619595, Coupling Machine Learning with Agent-Based Modeling to Design a Universal Influenza Vaccine (5R21AI169364-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10619595. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*