# Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolution

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2021 · $1,665,161

## Abstract

Project Summary/Abstract
It is hard to overstate the importance of monoclonal antibodies in the life sciences. Antibodies are critical tools in biomedical
research and diagnostics (e.g. western blotting, immunoprecipitation, cytometry, biomarker discovery, and histology), are
one of the most rapidly growing class of therapeutics, and are the basis for myriad new strategies in cancer therapy, such as
checkpoint inhibitors that are revolutionizing treatment. Unfortunately, current methods for the generation of custom
antibodies, including animal immunization and phage display, are slow, costly, inaccessible to most researchers, and often
unsuccessful. We propose Autonomously EvolvinG Yeast-displayed antibodieS (AEGYS), a system for the continuous and
rapid evolution of high-quality antibodies against custom antigens that requires only the simple culturing of yeast cells. We
believe this can be achieved by combining cutting-edge generative machine learning algorithms for antibody library design
with a new technology for in vivo continuous evolution and a yeast antigen-presenting cell that we will engineer. If
successful, AEGYS should have a transformative impact across the whole of biomedicine by turning monoclonal antibody
generation into a rapid, scalable, and accessible process where any lab with standard molecular biology capabilities can
generate custom antibodies on demand simply by “immunizing” a test tube of yeast cells with an antigen. We anticipate
that this democratization of antibody generation will also result in an explosion of crowdsourced antibody sequence data
that will train our machine learning algorithms to design better antibody libraries for AEGYS, starting a virtuous cycle. We
ourselves will use AEGYS to generate a panel of subtype- and conformation-specific nanobodies against biogenic amine
receptors including those that respond to acetylcholine, adrenaline, dopamine, and other neurotransmitters, so that we can
understand their role in neurobiology and addiction.!

## Key facts

- **NIH application ID:** 10260452
- **Project number:** 5R01CA260415-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Andrew Kruse
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,665,161
- **Award type:** 5
- **Project period:** 2020-09-10 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10260452

## Citation

> US National Institutes of Health, RePORTER application 10260452, Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolution (5R01CA260415-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10260452. Licensed CC0.

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