Collaborative Research: ACED: Accelerating Protein Engineering with Evolution-Guided Generative AI and a Self-Driving Biofoundry

NSF Award Search · 01002526DB NSF RESEARCH & RELATED ACTIVIT · $333,332 · view on nsf.gov ↗

Abstract

Proteins play a central role in many processes essential to life and have wide-ranging applications in medicine, energy, agriculture, and biotechnology. However, natural proteins are often not ideal for these practical uses. Protein engineering, a field that aims to design proteins with improved or novel functions, has transformed industries by creating tailored proteins. While traditional approaches, such as the Nobel Prize-recognized directed evolution method, have been remarkably successful in numerous protein engineering applications, they are typically slow, costly, and resource-intensive. This project seeks to advance protein engineering by combining cutting-edge artificial intelligence (AI) methods with advanced laboratory automation. By harnessing the power of AI to predict and design protein sequences and integrating it with an automated experimental platform, this research aims to greatly accelerate the discovery of new proteins, offering immense potential across multiple scientific domains with significant commercial and societal impact on medicine, biotechnology, energy, agriculture, chemical manufacturing, consumer products, and more. This project introduces a novel interdisciplinary approach leveraging recent AI breakthroughs in large language models and generative models, to guide protein function analysis and protein engineering, unlocking an unparalleled efficiency for functional protein discovery. The research focuses on developing new AI techniques tai

Key facts

NSF award ID
2435754
Awardee
Georgia Tech Research Corporation (GA)
SAM.gov UEI
EMW9FC8J3HN4
PI
Yunan Luo
Primary program
01002526DB NSF RESEARCH & RELATED ACTIVIT
All programs
Estimated total
$333,332
Funds obligated
$333,332
Transaction type
Standard Grant
Period
07/01/2025 → 12/31/2026