# Machine learning-enabled design of prototype pathogen vaccines and antibodies

> **NIH NIH U19** · UNIVERSITY OF WASHINGTON · 2024 · $41,075,241

## Abstract

PROJECT SUMMARY – OVERALL: MACHINE LEARNING-ENABLED DESIGN OF PROTOTYPE
 PATHOGEN VACCINES AND ANTIBODIES
We propose a highly synergistic Center focused on developing end-to-end strategies for pandemic
preparedness vaccine development for bunyaviruses and paramyxoviruses. Our Center brings together five
research institutions with complementary and synergistic expertise in computational protein design,
structure-based vaccine design, mRNA vaccines, structural biology, viral entry, viral diversity and evolution,
animal model development, high biosafety-level containment virology, vaccinology, and vaccine process
development and technology transfer. Our team has real-world experience in vaccine and biologics product
development in both academic and industry settings. Our Center comprises five Scientific Projects supported
by three Scientific Cores, an Administrative Core, and a Data Management Core. Our Scientific Projects
include: 1) Development of computational methods for vaccine and biologics design, 2) Fundamental research
on viral entry and receptors, 3) Antigen design, 4) Protein nanoparticle vaccine development, and 5) mRNA
vaccine development. We will structure our efforts in two phases: in Phase 1 (Years 1-3) we will focus on
developing vaccines for our prototype pathogens and in Phase 2 (Years 4-5) we will apply those learnings to
two new bunyaviruses and two new paramyxoviruses to demonstrate that our computational and experimental
approaches generalize across viral families. Our prototype pathogens are: Lassa virus (LASV; arenaviruses),
Rift Valley fever virus (RVFV; phenuiviruses), and Hendra virus (HeV; paramyxoviruses). We carefully selected
our prototypes as we believe they present specific vaccine design challenges which, if we are successful in
solving, will facilitate the development of vaccines against related viruses. Simultaneously, the antigens from
viruses in these three families have some similarities that will give rise to synergies in our design approaches.
The structure of our Center will allow maximal synergy between our groups in pursuit of its central output: to
define generalizable approaches and tools to develop vaccines and biologics for emerging pathogens with
pandemic potential.

## Key facts

- **NIH application ID:** 10861405
- **Project number:** 1U19AI181881-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Neil King
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $41,075,241
- **Award type:** 1
- **Project period:** 2024-08-12 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10861405

## Citation

> US National Institutes of Health, RePORTER application 10861405, Machine learning-enabled design of prototype pathogen vaccines and antibodies (1U19AI181881-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10861405. Licensed CC0.

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