# Project 1: Computational methods

> **NIH NIH U19** · UNIVERSITY OF WASHINGTON · 2024 · $3,753,275

## Abstract

PROJECT SUMMARY – PROJECT 1: COMPUTATIONAL METHODS
Project 1 will develop the general computational methods that underpin our Center’s approaches to vaccine
and antibody design. In Aim 1, the Baker and DiMaio labs will extend the RoseTTAFold2 network to enable
truly general biomolecular structure prediction, including full-atom models of protein post-translational
modifications, large symmetric or asymmetric protein complexes, and antibody-antigen complexes. These
methods will be critical for predicting the structures of viral glycoprotein antigens from our Center’s target viral
families as well as their receptors, both of which form symmetric and sometimes asymmetric complexes and
feature post-translational modifications such as N-linked glycosylation. This new All-Atom RoseTTAFold will
also be used as an oracle to evaluate how strongly the sequences of our Center’s designed antigens and
nanoparticle vaccines encode their target structures, allowing us to select the best designs for experimental
characterization. Finally, developing the ability to accurately predict the structures of antibody-antigen
complexes from sequence alone, a long-standing goal in structural biology and immunology, will be a powerful
tool for analyzing vaccine-elicited antibodies and will lay the foundation for de novo antibody design. In Aim 2,
the Baker lab will extend their machine learning methods for protein design, RF Diffusion and ProteinMPNN.
These methods provide two fundamental capabilities—de novo backbone generation and amino acid
sequence design—that will enable our Center to design antibodies and vaccines with unprecedented scale and
precision. In Aim 3, the DiMaio lab will combine existing machine-learning methods with newly developed
methods to extend the capabilities of cryo-electron microscopy. Specifically, these methods will enable
accurate structure determination from samples of proteins with various conformational states as well as
moderate-resolution datasets of antigens in complex with monoclonal or polyclonal antibodies. In our Center,
structures determined using these methods will guide the design of antigens stabilized in target conformations
that elicit potent neutralizing antibody responses, as well as monoclonal antibodies with exceptional
neutralizing potency and protective breadth. The computational methods developed in Project 1 will provide
our Center with unique tools that power our highly innovative approaches to antibody, antigen, and
nanoparticle vaccine design. All of the computational methods will be provided to the ReVAMPP network and
the broader scientific community as free, open-source software to speed biological research.

## Key facts

- **NIH application ID:** 10861411
- **Project number:** 1U19AI181881-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** DAVID BAKER
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $3,753,275
- **Award type:** 1
- **Project period:** 2024-08-12 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10861411

## Citation

> US National Institutes of Health, RePORTER application 10861411, Project 1: Computational methods (1U19AI181881-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10861411. Licensed CC0.

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